© 2016 International Monetary Fund
IMF Country Report No. 16/122
REPUBLIC OF SLOVENIA SELECTED ISSUES PAPER
This Selected Issues paper on the Republic of Slovenia was prepared by a staff team of
the International Monetary Fund as background documentation for the periodic
consultation with the member country. It is based on the information available at the
time it was completed on April 22, 2016.
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International Monetary Fund
Washington, D.C.
May 2016
REPUBLIC OF SLOVENIA
SELECTED ISSUES
Approved By European Department
Prepared By L. Dwight, I. Halikias, J. Ralyea and C. Visconti
EXPORT COMPETITIVENESS IN SLOVENIA ___________________________________________ 4
A. Introduction ___________________________________________________________________________ 4
B. Explaining developments in Slovenia’s external balances _____________________________ 5
C. Review of export performance in value added terms _________________________________ 7
D. Structural factors contributing to export performance ______________________________ 10
E. Conclusion ___________________________________________________________________________ 13
FIGURES
1. Export and GDP Growth _______________________________________________________________ 4
2. Main Trading Partners _________________________________________________________________ 5
3. Market Shares _________________________________________________________________________ 5
4. Developments in Slovenia’s Current Account Balance ________________________________ 6
5. Developments in Savings and Investment ____________________________________________ 7
6. Decomposition of Gross Export _______________________________________________________ 8
7. Value Added in Exports _______________________________________________________________ 9
8. Exports by Sector _____________________________________________________________________ 10
10. Human Capital ______________________________________________________________________ 11
11. Labor Markets _______________________________________________________________________ 12
12. International Capital Flows and Investment Positions ______________________________ 13
13. Foreign Investment Restrictions ____________________________________________________ 13
REFERENCES ___________________________________________________________________________ 15
CORPORATE FINANCIAL HEALTH AND INVESTMENT ______________________________ 16
A. Introduction __________________________________________________________________________ 16
B. Data and Methodology ______________________________________________________________ 18
C. Developments in firms’ financial condition in Slovenia and the CE-4 ________________ 19
D. Firms’ financial condition, investment and the effect of the crisis ___________________ 22
E. Policy options and conclusions _______________________________________________________ 26
CONTENTS
April 22, 2016
REPUBLIC OF SLOVENIA
2 INTERNATIONAL MONETARY FUND
FIGURES
1. Economic Trends and Corporate Financing __________________________________________ 17
2. Summary: Firm-level data ____________________________________________________________ 20
3. Slovenia ______________________________________________________________________________ 28
4. Czech Republic _______________________________________________________________________ 29
5. Hungary ______________________________________________________________________________ 30
6. Poland ________________________________________________________________________________ 31
7. Slovakia ______________________________________________________________________________ 32
TABLES
1. Point Estimates of Coefficients on Debt-Related Variables __________________________ 24
2. Optimal Debt Thresholds for Slovenia _______________________________________________ 25
3. Point Estimates of Coefficients _______________________________________________________ 26
ANNEXES
I. Definitions of Firm Financial Ratios and Variables ____________________________________ 34
II. Regression Results by Country and by Firm Size _____________________________________ 35
III.Treshold Effects ______________________________________________________________________ 36
REFERENCES ___________________________________________________________________________ 33
ECB QUANTITATIVE EASING: IMPLICATIONS FOR FISCAL POLICY _________________ 38
A. Introduction __________________________________________________________________________ 38
B. Fiscal “Windfall” of QE _______________________________________________________________ 39
C. Fiscal Reaction Functions – Empirical Analysis _______________________________________ 39
D. Fiscal Reaction Functions – Analytical Considerations _______________________________ 41
E. Policy Implications and Additional Constraints ______________________________________ 43
FIGURES
1. 10-Year Government Bond Yield and Spread vs. Germany___________________________ 45
2. Selected Euro Area Countries: Estimates of Funding Cost Savings From QE ________________ 45
3. Impulse Responses-Unconstrained VAR _____________________________________________ 46
4. Impulse Responses-Constrained VAR ________________________________________________ 47
5. Impulse Responses-Constrained VAR II ______________________________________________ 48
TABLE
1. Eurosystem Sovereign Debt Purchases under PSPP, ex Supranational Debt _________ 49
REFERENCES ___________________________________________________________________________ 50
FINANCIAL SECTOR DEVELOPMENT ISSUES AND PROSPECTS _____________________ 51
A. Introduction __________________________________________________________________________ 51
B. Financial System Structure and Recent Developments _______________________________ 52
C. Regulatory and Supervisory Framework _____________________________________________ 53
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 3
D. Indicators of Financial Development _________________________________________________ 54
E. Future Prospects _____________________________________________________________________ 56
BOXES
1. Financial Development Index ________________________________________________________ 61
2. Financial Development, Growth and Stability ________________________________________ 62
FIGURES
1. Private Sector Credit _________________________________________________________________ 63
2. Financial Development Index, Sub-Indices ___________________________________________ 64
3. Financial Development Sub-Indices Components ___________________________________ 65
4. Components of Financial Development Index, 2013 _________________________________ 66
5. Comparative Evolution of Institutions and Markets __________________________________ 67
6. Financial Development Impact _______________________________________________________ 68
REFERENCES ___________________________________________________________________________ 69
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4 INTERNATIONAL MONETARY FUND
EXPORT COMPETITIVENESS IN SLOVENIA1
A. Introduction
1. Slovenia’s exports have been the main contributor to GDP growth in recent years.
In particular, by 2015 exports of goods and services had increased by 20 percentage points of
GDP compared to their post-crisis low in 2009. This rapid increase in exports was due to an
increase in the nominal value of exports as nominal GDP was relatively steadily over this period
(Figure 1). This strong export performance raises the question of how Slovenia’s export sectors
have remained resilient despite weak domestic conditions in the wake of the global economic
and financial crisis. IMF (2013) finds that trade links between Germany and the four central
European countries (CE4)--the Czech Republic, Hungary, Poland, and Slovakia--via supply chains
contributed to rapid export and GDP growth in the CE4 countries. While Slovenia was not
included in the IMF (2013) study, trade statistics show that Slovenia is also well integrated in
European supply chains.
Figure 1. Slovenia: Export and GDP Growth
Sources: Haver Analytics; IMF staff calculations.
2. Slovenia trades intensively with Europe, with more than 90 percent of Slovenia’s
goods sold to other European countries. Germany is Slovenia’s largest export market, followed
by Italy and Austria. Slovenia’s top five export markets bought about half of Slovenia’s export
products (Figure 2), with the other half going mainly to emerging European countries and
countries in emerging Asia and the Middle East. Slovenia’s exports to Germany have remained
stable at about 20 percent of total exports over the past ten years, likely due to Germany’s
dominant position in European supply chains. While trade shares remained relatively constant,
exports to Germany grew rapidly in value terms. Total exports in percent of GDP have also grown
in recently years.
1 Prepared by Ruo Chen and Lawrence Dwight (SPR)
-30
-20
-10
0
10
20
30
2001 2003 2005 2007 2009 2011 2013 2015
Exports of goods Exports of services
Imports Consumption
Gross capital formation RGDP
Contributions to Real GDP Growth(year-on-year percent change)
0
20
40
60
80
100
120
0
5
10
15
20
25
30
2001 2003 2005 2007 2009 2011 2013 2015
Perc
en
t
Eu
ro B
illio
ns
Export value
Exports/GDP (RHS)
Goods Exports
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 5
Figure 2. Slovenia: Main Trading Partners
Sources: DOTS statistics; IMF staff calculations.
3. Slovenia has maintained its trade shares of both German and World imports, but
unlike most of the CE4 countries it shares have not increased significantly (Figure 3). Among
the CE4 countries, Poland and the Slovak Republic have shown the largest gains in market shares.
Figure 3. Slovenia: Market Shares
Sources: DOTS statistics; IMF staff calculations.
B. Explaining developments in Slovenia’s external balances
4. Strong growth in trading partners is one explanation for the considerable
improvement in Slovenia’s current account balance. Slovenia’s current account switched from
a deficit of 5 percent of GDP in 2008 to a surplus of 7¼ percent of GDP in 2015 (Figure 4). This
was largely the result of a surge in exports that paralleled an increase in imports of Slovenia’s
major export markets. For example, from 2008 to 2015, total German and Austrian imports rose
by 23 and 13 percent, respectively, while Italian imports dropped by 5 percent. Not surprisingly,
0
10
20
30
40
50
60
70
2001 2003 2005 2007 2009 2011 2013 2015
Germany Italy Austria Croatia France
Top 5 Export Markets(in percent of total exports)
50
100
150
200
250
2001 2003 2005 2007 2009 2011 2013 2015
Slovak Rep
Poland
Czech Rep
Slovenia
Hungary
Increases in Shares of German Imports(index, 2001 = 100)
50
100
150
200
250
2001 2003 2005 2007 2009 2011 2013 2015
Slovak Rep
Poland
Czech Rep
Slovenia
Hungary
Shares in World Imports(in percent)
0
2
4
6
8
0
4
8
12
16
2001 2003 2005 2007 2009 2011 2013 2015
Billio
ns
of
Eu
ro
Perc
en
t o
f G
DP
Exports to Germany Imports from Germany
Exports/GDP Imports/GDP
Trade with Germany
REPUBLIC OF SLOVENIA
6 INTERNATIONAL MONETARY FUND
Slovenia’s exports of goods have increased by 13 percent since 2008, close to the weighted
average of import growth in these three major trading partners.
5. During the same period, Slovenia’s imports remained relatively flat in line with slow
GDP growth. For 2008–15, Slovenia’s nominal GDP rose only by 1½ percent, with real demand
for imports rising by only 2¼ percent. And while trading partners were recovering from the
global crisis, Slovenia was hit by a banking crisis in 2012–13, resulting in a nominal decline in
both GDP and imports during this period.
Figure 4. Developments in Slovenia’s Current Account Balance
6. From a savings-investment perspective, the improvement in the current account
went through several phases, first primarily stemming from falling private investment and
then due to rising income (Figure 5). Immediately after the global financial crisis (2007–09),
income growth remained positive with consumption growth even stronger, leading to a fall in
savings. However, a sharp fall in private investment more than offset the fall in savings, leading
to an improvement in the current account balance. In 2009–11, increases in income and
consumption were small and offsetting and investment was slightly negative, leading to little
change in the size of the current account. During Slovenia’s banking crisis (2011–13), income and
consumption fell commensurately so that there was little change in savings. However, a
-10
-5
0
5
10
15
2001 2003 2005 2007 2009 2011 2013 2015
Goods balance Service balance
Primary income Secondary income
Current account
Current Account(in percent of GDP)
Sources: Bank of Slovenia; Haver Analytics; and IMF staff estimates.
-10
0
10
20
30
40
40
50
60
70
80
90
100
2001 2003 2005 2007 2009 2011 2013 2015
Exports of goods & services
Imports of goods & services
Trade balance (RHS)
Trade Balance, Goods and Services(in percent of GDP)
Sources: Bank of Slovenia; Haver Analytics; and IMF staff estimates.
0
20
40
60
80
100
2001 2003 2005 2007 2009 2011 2013 2015
Exports of services
Exports of goods
Exports of Goods and Services(in percent of GDP)
Sources: Bank of Slovenia; Haver Analytics; and IMF staff estimates.
75
100
125
150
2007 2008 2009 2010 2011 2012 2013 2014 2015
Imports: Germany
Imports: Austria
Imports: Italy
Exports: Slovenia
Partner Imports and Slovenian Exports, Post-Crisis(Index of Nominal Value, 2007 = 100)
Sources: Bank of Slovenia; Haver Analytics; and IMF staff estimates.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 7
moderate fall in private investment caused the current account to improve significantly. Finally, in
recent years (2014–15), the improvement has been driven by significant income growth (led by
exports) and hence savings. Investment growth turned positive for the first time since the start of
the global crisis, but lagged growth in income. In sum, from 2007 to 2013 the 7 percent of GDP
improvement in the current account balance resulted primarily from a fall in private investment
(from 30 to 17 percent of GDP), while in the last two years the 4 percent of GDP improvement in
the current account balance resulted primarily from export-led growth.
7. So why hasn’t private investment rebounded more strongly given strong exports,
GDP growth, and a large current account surplus? As discussed in the chapter on corporate
financial health, many companies are still struggling with an overhang of corporate debt. This
hinders their ability to invest. Exporters have been less affected than other firms because they are
more likely to be large companies with substantial retained earnings, access to external
financing, and/or easier access to domestic bank financing. In addition, exporters can post good
collateral in the form of export earnings. In contrast, unreformed domestic firms (with relatively
high debts and relatively low equity and profits) have suffered a deterioration in the quality of
their collateral.
Figure 5. Contributions to Changes in Savings and Investment
(in percent of GDP)
C. Review of export performance in value added terms
8. Traditional trade statistics tend to overestimate domestic value added in exports
due to the inclusion of imported intermediate goods, which represent foreign value added.
We follow the framework for the decomposition of gross exports described in Koopman et al.
(2011), Rahman and Zhao (2013), and IMF (2013). As shown in Figure 6, gross exports can be
decomposed into five categories based on the origin of value added and the stage of
production: (1) domestic value added in final goods, (2) domestic value added in intermediate
goods not processed for further exports, (3) domestic value added in intermediate goods
-10
-5
0
5
10
15
2007-09 2009-11 2011-13 2013-15
Nominal GDP Private Consumption Public Consumption
Private Investment Public Investment S - I (RHS)
Sources: Bank of Slovenia; Haver Analytics; and IMF staff estimates.
REPUBLIC OF SLOVENIA
8 INTERNATIONAL MONETARY FUND
processed for export to third countries, (4) domestic value added in exports to other countries
but later re-imported by the home country for export, and (5) imported value added used as
inputs into domestic exports (also known as foreign value added). Components (1)-(4) represent
domestic value added in gross exports, i.e. the real contribution of exports to the domestic
economy in terms of income and employment. Components (3)-(5) represent domestic and
foreign inputs into global and regional supply chains, with components (3)-(4) representing
domestic value added into supply chains. Growth of supply chains would result in parallel
increases in domestic and foreign value added in exports.
Figure 6. Decomposition of Gross Export
Sources: Koopman et al. (2011), Rahman and Zhao (2013), and IMF (2013).
9. On the import side, foreign value added in Slovenia’s exports has increased over
the last 20 years, but the increase has been smaller than for each of the CE4 countries,
indicating less integration on imports (Figure 7). All CE4 countries showed increases in the
shares of foreign value added in exports by at least 12 percentage points, while the share of
foreign value added in Slovenia’s exports increased only five percentage points. Germany has the
largest share of foreign value added in each country’s gross exports (except for the Slovak
Republic where Russia’s value added is largest), indicating strong trade links through regional
supply chains. In contrast with the CE4 countries where Germany’s share of value added has
increased, Germany’s share of valued added in Slovenia’s exports has dropped slightly. On the
other hand, Slovenia also has strong trade links with Italy and intermediate goods from Italy
make up the second largest share of foreign value added in Slovenia’s exports.
10. On the export side, shares of each country’s value added in German exports have
increased significantly, suggesting substantial increases in integration since 1995. In fact,
Slovenia’s exports of intermediate goods to Germany have increased more (by 2 percentage
points of total exports) than for the CE4 countries (by 1 percentage point). The share of
intermediate goods exports to Germany (5 percent of total exports) is similar to the share for the
Gross Exports
Domestic
Value Added
(DVA)
DVA in final
goods exports
(1)
DVA in
intermediate
goods not
processed for
export
(2)
DVA in
intermediate
goods
processed for
export
(3)
DVA in
intermediate
goods
exported but
re-imported
(4)
Foreign Value
Added (FVA)
FVA in
intermediate
goods included
in domestic
exports
(5)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 9
CE4 countries (4–6 percent of total exports). Thus, the contribution of Slovenia’s integration in
the German supply chain to its export performance is on a par with other countries in the region.
Figure 7. Value Added in Exports
1 Measures how important a country’s contribution to German exports is to the country
(e.g. for Slovenia = value of Slovenia’s exports incorporated to Germany’s exports / Slovenia’s total exports).
Sources: OECD-WTO Trade in Value Added database; IMF staff calculations
11. The transportation, electrical equipment, machinery, and chemicals sectors are the
most important for the CE4 countries and Slovenia in the German supply chain. The IMF’s
2013 Cluster Report on the German-Central European Supply Chain analyzes the German
automobile industry, explains the factors driving integration of the CE4’s automotive sectors, and
evaluates the contribution of the automotive sector to their economies. In Slovenia, the transport
equipment, machinery, and pharmaceutical sectors make up almost half of the total exports and
have been the main contributors to export growth (Figure 8).
12. The sectoral decomposition shows that foreign value added of transport equipment
has increased as a share of Slovenia’s total exports, but less than most of the CE4
countries. In Slovenia, motor vehicles are the largest category of transport equipment exports
accounting for almost 9 percent of total exports.2 The share of foreign value added in Slovenia’s
transport equipment exports has increased by about 6 percentage points over the last 20 years,
similar to the Czech Republic and Hungary but lower than in Poland and the Slovak Republic. The
share of foreign value added in Hungary, Slovakia, and Slovenia were already relatively high in
1995, comprising more than half of the gross value of transport equipment exports. Although
Slovenia’s exports of transport equipment increased more rapidly than Slovenia’s total exports,
they still lagged behind the increase in transport equipment exports of the CE4 countries. In the
largest category (automobiles), there is room for additional integration into the German supply
chains.
2 Transport equipment sector includes the manufacture of aircraft and other aerospace equipment, railroad
equipment, motor vehicles and auto parts, motorcycles and bicycles, as well as the building, repairing and
breaking of ships.
68 64 7055
70
52
84
68 6853
76
7
9
6
10
4
6 5
6
25 3024
3623
39
1226 26
40
0
10
20
30
40
50
60
70
80
90
100
1995 2011 1995 2011 1995 2011 1995 2011 1995 2011
Slovenia Czech Republic Hungary Poland Slovak
Republic
Domestic VA German VA Other countries' VA
Source of Value Added in Gross Exports(in percent of total exports)
0
2
4
6
8
Slovenia Czech
Republic
Hungary Poland Slovak
Republic
1995 2011
Contribution to Value Added of German Exports
as a Share of Country Exports1
(in percent)
REPUBLIC OF SLOVENIA
10 INTERNATIONAL MONETARY FUND
Figure 8. Slovenia: Exports by Sector
Sources: WITS database; IMF staff calculations
Figure 9. Value Added in Transport Equipment Exports
1 Measures how important a country’s contribution to German exports of transport equipment is to the country
(e.g. for Slovenia = value of Slovenia’s transport equipment exports incorporated to Germany’s exports /
Slovenia’s total exports of transport equipment).
Sources: OECD-WTO Trade in Value Added database; IMF staff calculations
D. Structural factors contributing to export performance
13. Improving labor markets, boosting foreign investment, and enhancing vocational
training would improve Slovenia’s competitiveness. Empirical research has identified these
three structural components as important for export performance. Rahman and Zhao (2013)
show that a country’s strong export performance globally and in Europe since the late 1990s has
been associated with successful integration with supply chains. Rahman et al. (2015) further
explore export performance in the European Union single market and find that export
competitiveness depends on successful structural reforms in higher education, upgrading skills,
wage incentives, and an environment that supports foreign investment. This section compares
Slovenia’s development in these areas with the CE4 countries.
0
20
40
60
80
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Machinery Manufactures Chemicals
Misc manufactures Fuels & lubricants Others
Sectoral Composition of Exports(in percent of total exports)
-20
-15
-10
-5
0
5
10
15
20
2001 2003 2005 2007 2009 2011 2013
Machinery Manufactures
Chemicals Misc manufactures
Fuels & lubricants Others
Total
Sectoral Contributions to Export Growth(year-on-year percent change)
48 4456
47 43 39
76
5345 40
1210
12
1415 17
7
1117
12
4047
3239 42 44
17
36 3848
0
10
20
30
40
50
60
70
80
90
100
1995 2011 1995 2011 1995 2011 1995 2011 1995 2011
Slovenia Czech Republic Hungary Poland Slovak
Republic
Domestic VA German VA Other countries' VA
Value Added in Exports of Transport Equipment(in percent of transport equipment exports)
0
5
10
15
20
Slovenia Czech Republic Hungary Poland Slovak Republic
1995 2011
Transport Equipment: Country VA in German Exports(in percent)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 11
Human capital
14. Better human capital supports incorporation of new technology, integration into
supply chains, and improvement in exports. Rahman et al. (2015) find that two proxy
indicators for human capital--higher education and the upgrading of vocational training and
skills—explain close to 50 percent of export performance in the European Union. On measures of
human capital, Slovenia compares favorably with the CE4 countries. The share of young people
with secondary or higher education are similar to the levels in the Czech Republic, Poland, and
Slovakia, and higher than the level in Hungary and the EU average (Figure 10). Slovenia has larger
shares of employees participating in continuous vocational trainings than that in Hungary,
Poland, and the EU averages. To utilize its skilled labor force, Slovenia also needs efficient labor
market and supporting foreign investment environment.
Figure 10. Human Capital
Sources: Eurostat; IMF staff calculations
Labor markets
15. The development of supply chains has been driven in part by firms outsourcing
their production processes in order to seek efficiency gains through differences in wages. It
is critical for host countries to provide a competitive wage structure. Slovenia does not show a
comparative advantage in unit labor costs relative to the CE4 countries. For example, Slovenia’s
unit labor costs remain higher than the CE4 countries in both the whole economy and in industry
(Figure 11), although recent data suggest an improvement.
16. Well-functioning labor markets are also critical to ensure efficient allocation of
labor and provide incentives to work. Rahman et al. (2015) uses two proxies, the inactivity trap
and relative minimum wages, to measure labor market efficiency. The inactivity trap captures
incentives to stay out of the work force due to either labor taxes or loss of social benefits.3 The
inactivity trap indicator suggests that Slovenia provides weaker incentives to work relative to CE4
3 Inactivity trap = 1 – (in-work net pay – out-of-work net pay) / (in-work gross pay – out-of-work gross pay).
60
70
80
90
100
2001 2003 2005 2007 2009 2011 2013
Slovak Rep
Czech Rep
Poland
Slovenia
Hungary
EU average
Upper Secondary or Tertiary Educational Attainment(in percent of population aged 20-24 years)
0
20
40
60
80
Czech Rep Slovak Rep Slovenia EU average Poland
All Activities Industry (excl. construction)
Participation in Continuous Vocational Training(in percent of total employment, 2010)
REPUBLIC OF SLOVENIA
12 INTERNATIONAL MONETARY FUND
countries. Countries with low minimum wages, a measure of the cost of employing low-skilled
labor, tend to have a comparative advantage in labor-intensive exports in their trade with the EU
(Rahman et al., 2015). Although Slovenia’s minimum wage is low relative to that of the CE4
countries, relatively high unit labor costs offset a comparative advantage in labor-intensive
exports. To provide an attractive labor market for multinational companies, Slovenia needs to
reduce its unit labor costs (via increased productivity) and disincentives to work.
Figure 11. Labor Markets
Sources: Lisbon Assessment Framework (LAF) database; OECD statistics; IMF staff calculations.
Foreign investment environment
17. Foreign direct investment (FDI), particularly greenfield investment, has been a key
channel to expand production in foreign countries. For example, the evolution of the German
supply chain has been supported by large inflows of FDI to the CE4 countries (IMF, 2013). FDI is
usually a relatively stable source of external funding, holds up well during external crises, and is
less prone to sudden stops. FDI inflows were the main component in net capital inflows to the
CE4 countries before the financial crisis, averaging 4½ percent of GDP per year. In contrast, FDI
inflows to Slovenia were less than one percent of GDP per year before the crisis (Figure 12). As a
result, the stock of FDI in the CE4 countries has been around 40 to 50 percent of GDP. The
comparable figure for Slovenia has been less than 15 percent of GDP over the last decade.
Another index, from the Economic Freedom of the World (EFW) 2015 report, indicates that there
is a perception that Slovenia has more restrictions on foreign ownership and investment than the
CE4 countries and that these restrictions have increased since the global financial crisis (Figure
13). The EFW’s index of foreign ownership and investment restrictions is based on two surveys
from the World Economic Forum’s Global Competitiveness Report: (i) the prevalence of foreign
ownership and (ii) regulatory restrictions on international capital flows. In Slovenia’s case, the
perception of greater restrictions on foreign investment may have contributed to a lower level of
foreign ownership. Rahman et al. (2015) conclude that Slovenia should make its foreign
ownership regime more conducive for investors.
0.2
0.4
0.6
0.8
1.0
2001 2003 2005 2007 2009 2011 2013
Slovenia
Hungary
Poland
Czech Rep
Slovak Rep
Unit Labor Cost - Total Economy(level, the ratio of total labour costs to real output)
0.2
0.4
0.6
0.8
1
2001 2003 2005 2007 2009 2011 2013
Slovenia
Hungary
Poland
Czech Rep
Slovak Rep
Unit Labor Cost - Industry(level, the ratio of total labour costs to real output)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 13
Figure 12. International Capital Flows and Investment Positions
Sources: BOPS statistics; IMF staff calculations.
Figure 13. Economic Freedom of the World:
Foreign Ownership / Investment Restrictions Sub-Index
(survey based, lower = greater restrictions)
Sources: Economic Freedom of the World 2015 database; IMF staff calculations.
E. Conclusion
18. Slovenia’s current account surplus reflects strong integration in European supply
chains, maintained price competitiveness, and lackluster domestic demand. Slovenia’s
current account balance has risen dramatically from a deficit of 5 percent of GDP in 2008 to over
7 percent of GDP in 2015. From a trade perspective, the increase was driven by a significant
increase in exports (in line with the growth of imports of Slovenia’s trading partners) while
imports have remained relatively flat (in line with low GDP growth in Slovenia). From a savings-
investment perspective, the 2008-2013 dynamics were driven by a large decline in private
investment’s share of GDP, while improvement in the last two years has been driven by a sharp
-20
-15
-10
-5
0
5
10
15
20
2001 2003 2005 2007 2009 2011 2013 2015
FDI
Portfolio
Other
Total
Slovenia: Net Capital Inflows(in percent of GDP)
-20
-15
-10
-5
0
5
10
15
20
2001 2003 2005 2007 2009 2011 2013
FDI
Portfolio
Other
Total
CE4: Average Net Capital Inflows(in percent of GDP)
0
2
4
6
8
10
2001 2003 2005 2007 2009 2011 2013
Slovak RepCzech RepHungaryPolandSlovenia
REPUBLIC OF SLOVENIA
14 INTERNATIONAL MONETARY FUND
increase in private savings. The recovery in private investment has lagged due in part to the
corporate debt overhang. In terms of integration, foreign imports have contributed very
modestly to Slovenia’s exports. However, on the export side Slovenian goods have seen a
significant increase in their incorporation into the European supply chain, particularly for
Germany.
19. Nevertheless, additional steps could improve labor markets, boost foreign
investment, and enhance training to increase Slovenia’s competitiveness further. Studies
have shown that structural reforms are associated with strong export growth. In this regard, steps
to address areas where Slovenia lags its peers could further improve competitiveness. These
steps include further boosting human capital (e.g. by improving the quality of education to
address demands of the marketplace), reforming labor markets (e.g. by reducing labor costs and
disincentives to work), and increasing foreign direct investment (e.g. by reducing perceptions of a
relatively restrictive investment environment).
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 15
References
Koopman, Robert, Zhi Wang, and Shang-Jin Wei, 2012, “Tracing Value-Added and Double
Counting in Gross Exports”, NBER Working Paper 18579.
International Monetary Fund, 2013. German-Central European Supply Chains – Cluster Report,
(Washington, DC).
International Monetary Fund, 2015. Central and Eastern Europe: New Member States (NMS)
Policy Forum, 2014, Staff Report on Cluster Consultations – Common Policy Frameworks and
Challenges, (Washington, DC).
Rahman, Jesmin, Ara Stepanyan, Jessie Yang and Li Zeng, 2015, “Exports in a Tariff-Free
Environment: What Structural Reforms Matter? Evidence from the European Union Single
Market” IMF Working Papers Np. 15/187, (Washington, DC).
Rahman, Jesmin and Tianli Zhao, 2013,"Export Performance in Europe: What Do We Know from
Supply Chains?” IMF Working Papers No. 13/62, (Washington, DC).
REPUBLIC OF SLOVENIA
16 INTERNATIONAL MONETARY FUND
CORPORATE FINANCIAL HEALTH AND INVESTMENT1
A. Introduction
1. When the global asset bubble burst in 2008, credit and investment collapsed in
central Europe (Figure 1). Preceding the crisis, bank credit in Slovenia and four central
European countries (Czech Republic, Hungary, Poland, and Slovakia, collectively the CE-4) fueled
corporate investment. When global credit markets froze in late 2008, bank financing dried up,
precipitating credit- and investment-starved recessions in Slovenia and the CE-4, except Poland.
Slovenia and Hungary, where non-financial corporates were the most indebted, were hit the
hardest, with domestic banks in Slovenia requiring a public-sector bailout in 2013. Today, private
investment remains well below pre-bubble levels in Slovenia, and investment in other countries
have only returned to 2004 levels, despite a resumption of growth and historically low policy
rates.
2. Today’s low interest rates are supportive of corporate investment. In theory, a firm’s
decision to investment depends on the risk-adjusted expected return of the investment. If the
return exceeds a pre-determined hurdle rate (the discount rate applied to projected cash flows
associated with the investment), it is assumed financing will be available for the project and it will
be undertaken. The hurdle rate typically embodies a firm’s weighted average cost of capital,
managers’ and owners’ degree of risk aversion, and risks specific to the investment, including
uncertainty surrounding cash flow projections. Changes in policy interest rates affect the cost of
firm debt, and indirectly the cost of firm equity, thereby influencing a firm’s hurdle rate.
Specifically, lower policy interest rates can induce more investment spending as a greater
number of potential investments become financially attractive to undertake.
3. However, poor corporate financial health can impair monetary policy transmission.
In the presence of financial frictions, a firm’s financial health could play a larger role in
determining its investment decisions and the availability of related financing (see Martinez-
Carrascal and Ferrando, 2008 for a review of the literature). Indeed, studies have found that a
firm’s balance sheet strength, profitability, and the availability of liquid assets are significant
determinants of investment (e.g., see Fazzari, Hubbard, and Perterson, 1988). In essence, high
corporate debt burdens weigh on the capacity and desire of firms to finance an investment.2 As a
result, investment decisions and available financing for investment become less responsive to
reductions in interest rates.
1 Prepared by John Ralyea with assistance from Luisa Calixto and Tingyun Chen.
2 References to a firm’s capacity or ability to investment include the availability of internal financing such as
retained earnings or fresh equity and external financing provided by bank and non-bank entities.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 17
Figure 1. Economic trends and corporate financing
Sources: Eurostat, FSI, Haver Analytics, and IFS.
1/ 2015Q3 value is used instead.
-10
-5
0
5
10
152004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Slovenia
Czech Republic
Hungary
Poland
Slovakia
Real GDP Growth
(percent)
Figure 1. Economic trends and corporate financing
70
90
110
130
150
170
190
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Slovenia
Czech Republic 1/
Hungary 1/
Poland
Slovakia 1/
Real Investment
(Index, 2004 = 100)
70
80
90
100
110
120
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
15Q
3
Slovenia Czech Republic
Hungary Poland
Slovakia
Non-Financial Corporate Debt to Assets
(Percent)
80
90
100
110
120
130
140
150
160
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
15Q
3
Slovenia Czech Republic
Hungary Poland
Slovakia
Non-Financial Corporate Debt to Equity
(Percent)
60
80
100
120
140
160
180
200
220
240
260
2004Q
1
2005Q
1
2006Q
1
2007Q
1
2008Q
1
2009Q
1
2010Q
1
2011Q
1
2012Q
1
2013Q
1
2014Q
1
2015Q
1
Slovenia
Czech Republic
Hungary
Poland
Slovakia
Real Credit to Private Sector
(Index, 2006 = 100)
20132013
2014 2011 2013
0
2
4
6
8
10
12
14
16
18
20
SV
N
HU
N
CZ
E
SV
K
PO
L
Peak of NPLs to Total Loans During 2007-15Q3
(Percent)
REPUBLIC OF SLOVENIA
18 INTERNATIONAL MONETARY FUND
4. Against this background, this paper assesses corporate financial health in Slovenia
and the CE-4, using firm-level data, and the potential effect on investment. In particular, the
paper addresses the following questions: i) How has the financial health of non-financial
corporates faired over the last nine years, in terms of profitability, liquidity, and indebtedness, in
Slovenia and the CE-4?; ii) Has a structural change occurred following the financial crisis in either
a firm’s willingness or ability to undertake investments given its financial health?; and iii) Is there
a threshold of indebtedness that leads to lower corporate investment?
5. The rest of the paper proceeds as follows: Section B describes the firm level data and
methodology used in the analysis; Section C reviews trends in the financial health of firms in the
countries under study; Section D presents an econometric model linking a firm’s investment to its
financial health and broader economic and financial conditions; and Section E offers policy
considerations.
B. Data and Methodology
6. The analysis relies on a large dataset of harmonized financial data for firms in
Slovenia and the CE-4. The initial sample is the set of all non-financial firms (NFCs) for which
financial and operating data are available on the Orbis database. The firm-year sample sizes
average between 30,000–55,000 for Slovenia, the Czech Republic, Poland and Slovakia and
around 130,000 for Hungary. The database includes a large portion of small- and micro-sized
firms not readily available elsewhere, allowing for greater coverage of the non-financial sector.
Only firm-year observations that contain positive values for tangible fixed assets are included in
the analysis.
7. The evolution of corporate financial health since 2006 is assessed based on
indicators of profitability, debt, and debt service capability. The following financial ratios are
constructed from detailed financial statements: return on total assets, profit margin, cash-to-
assets, debt-to-assets, debt-to-equity, debt-to-cash flow, and interest coverage. These indicators
capture a firm’s financial prospects and balance sheet strength, which, in turn, influence a firm’s
ability and willingness to use internal funds and external financing for investment. Specifically,
return on assets and profit margins are proxies for the profitability of a firm and its growth
prospects. Debt-to-assets reflects a firm’s degree of leverage, while debt-to-cash flow and the
interest coverage ratio are indicators of a firm’s ability to service its debt. Firms are also classified
based on size, sector of operation, and initial level of indebtedness (See Annex I for definitions of
ratios and firm size). For the latter, firms with financial debt-to-asset ratios that exceed the
median for firms in the same country are classified as high-leverage firms.
8. A standard investment model is estimated to examine the relationship between a
firm’s financial health, its access to financing, and its investment outlays. The variation over
time and across firms in profitability, liquidity, and indebtedness is exploited to help explain the
variation in firm investment. Of interest is whether the 2008 financial crisis induced a change in
the sensitivity of a firm’s investment spending to indicators of its financial health. Estimations are
also run to determine if firm size influences the capacity or desire to invest. Moreover, the impact
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 19
of changes in bank lending conditions is modeled directly for Slovenia,3 where standards
tightened considerably following the global and domestic bank crises, and only began to loosen
in late 2015. Threshold levels (the ratio above which debt begins to influence investment
negatively) for debt-to-assets and debt-to-cash flow were estimated for Slovenia as well.
C. Developments in firms’ financial condition in Slovenia and the CE-4
9. Corporates are slowly repairing their financial health (Figure 2). The 2008 global
financial crisis exposed the underlying vulnerabilities of corporate balance sheets in Slovenia and
the CE-4 that grew fat on borrowing in the pre-crisis period. The liquidity shock and concomitant
recession induced a deterioration in firm financial indicators across countries and firm sizes.
Profitability fell and firms’ debt burdens spiked. By the end of 2014, many financial ratios had
returned close to pre-crisis levels, as highly indebted firms deleveraged and growth picked up in
2014. 4 However, corporate investment remained subdued.
Profitability and liquidity
10. Firm profitability and liquidity ratios are pointing upwards (Figures 3–7). After falling
significantly in 2008 and 2009, firm profitability stabilized before picking up in 2014. This was the
case in all countries except Poland, where return on assets continued to fall gradually until 2013
though the overall profitability of Polish firms generally remained higher than in the other
countries. Micro-sized firms were the least profitable throughout the post-crisis period, except in
Slovenia, where the profitability of micro enterprises was higher and rebounded earlier than
other firms. Turning to liquidity, Czech, Hungarian, and Slovakian firms are the most liquid with
median aggregate cash-to-asset ratios greater than 10 percent. Slovenia firms are the least
liquid, despite a general improvement in liquidity after the 2008 financial crisis, particularly in
micro-sized firms.
3 ECB bank lending survey data that covers the crisis period and afterwards is only available for Slovenia.
4 The reported summary statistics are based on firm-level observations excluding the top and bottom 5th
percentile of values for each indicator to avoid distortions from extreme outliers. Eurostat data presented in
Figure 1, suggests that these trends continued in 2015.
REPUBLIC OF SLOVENIA
20 INTERNATIONAL MONETARY FUND
Figure 2. Summary: Firm-level data, 2006–14
Sources: Orbis; IMF staff calculations.
CZ= Czech Republic; HU=Hungary; SK=Slovakia;SI=Slovenia
-15
-5
5
15
25
35
-15
-5
5
15
25
35
2006 2007 2008 2009 2010 2011 2012 2013 2014
CZ HU
PL SK
SI
Investment
(Median, percent change in tangible fixed assets)
30
40
50
60
70
30
40
50
60
70
2006 2007 2008 2009 2010 2011 2012 2013 2014
CZ HU
PL SK
SI
Debt-to-assets
(Median, percent)
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
2006 2007 2008 2009 2010 2011 2012 2013 2014
CZ HU
PL SK
SI
Return on assets
(Median, percent)
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
2006 2007 2008 2009 2010 2011 2012 2013 2014
CZ HU
PL SK
SI
Cash-to-assets
(Median, percent)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 21
Leverage
Financial debt-to-assets
11. Deleveraging has been gradual. The median level of debt-to-assets in Slovenia, the
Czech Republic, and Slovakia hovered around 60-70 percent throughout 2007–14, while the
median ratio was closer to 50 percent and 35 percent in Poland and Hungary, respectively. The
ratio for all firms decreased somewhat toward the end of the period in each country except
Slovakia where it jumped in 2009 and stayed at the more elevated level through 2014. However,
the aggregate figures mask significant differences among firms of different sizes. In all countries
except Slovakia, micro-sized firms have considerably higher debt burdens than larger firms
throughout most of the post-crisis period. The evolution of firm indebtedness also varies
depending on the initial level of firm leverage. Highly leveraged firms shed more debt, while
those with less debt maintained or increased their leverage.
Debt overhang (excessive debt)
12. Micro-enterprises are still
burdened with excessive debt. In
aggregate, firms in the Czech Republic,
Poland, and Slovenia have reduced their
debt overhang to pre-crisis levels, while
Hungarian and Slovakian firms generally
have the most excessive debt. It is worth
noting, that developments in the measure of
excessive debt (defined in this paper as
financial debt greater than 5 times earnings
before interest, taxes, depreciation, and
amortization (EBITDA)) are sensitive to
annual fluctuations in cash flow from
operations.5 Thus, looking solely at micro
enterprises Slovenia firms suffer the least
from excessive debt and Polish ones join
their Hungarian counterparts with the largest amounts of excessive debt since 2011.
Nonetheless, 45 percent of all sampled Slovenian firms faced excessive debt levels in 2014.
5 EBITDA is an approximate measure of operating cash flow. The measure of excessive debt is consistent with
empirical results from a threshold analysis of debt-to-EBITDA on investment. The threshold level for debt-to-
EBITDA at which the marginal return in terms of more investment begins to diminish is 4 for all firms in Slovenia.
See Annex III.
15
25
35
45
55
65
15
25
35
45
55
65
2006 2007 2008 2009 2010 2011 2012 2013 2014
Excessive debt (all firms)
(Percent of annual debt > 5x EBITDA)
SI CZ HU PL SK
REPUBLIC OF SLOVENIA
22 INTERNATIONAL MONETARY FUND
Debt service capacity
13. Corporate capacity to pay
interest generally improved in 2013–14.
However, a significant number of firms (10
to 30 percent of the sampled firms,
depending on the country) have interest
coverage ratios (EBITDA over financial
expense) below one.6 In Slovenia and
Slovakia, 2/5 of micro enterprises do not
have the cash flow to cover annual interest
payments. These firms have to rely on other
sources of financing such as cash balances,
asset sales, or credit lines to cover annual
interest payments, suggesting that many
firms still face significant financing
constraints. However, with the exception of micro-sized firms, Slovenian firms in general can
more easily cover interest payments out of cash flow from operations than their peers in
comparator countries.
D. Firms’ financial condition, investment and the effect of the crisis
14. With the onset of the financial crisis, investment by companies in Slovenian and the
CE-4 fell significantly and continued to contract through 2014. 7 The sharp decline across the
board can be largely explained by the widespread fall in aggregate demand after the 2008
financial crisis as documented in Chapter 4 of the April 2015 World Economic Outlook (IMF,
2015c). However, the contraction in economic activity does not explain the entire fall in corporate
investment nor the duration of the investment slump, suggesting that other factors also
contributed to the decline.
15. The financial crisis may have changed the sensitivity of firms’ investment to its
financial health. Studies of Slovenian firms have found that weak balance sheets, as indicated by
high debt burdens e.g., a debt-to-EBITDA ratio greater than 5, an interest coverage ratio less
than 1.5 (Damijan, 2014), or a firm financing structure heavily reliant on debt are more
susceptible to financing constraints. With the exception of micro-enterprises, indicators of
financial health have broadly returned to their pre-crisis levels, yet investment has not recovered.
This suggests the possibility of a more cautious approach by corporates in assessing their ability
6 With the potential for “sudden stops” during and after a financial crisis, it would be preferable to analyze cash
flow to debt service, given the high probability that principal would not be rolled over. However, current data
limitations regarding debt repayment profiles for firms prevent calculation of this statistic across countries.
7 Corporate investment is measured as the change in total tangible assets between t and t-1 plus depreciation
and amortization over total tangible assets at t-1.
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
2006 2007 2008 2009 2010 2011 2012 2013 2014
Interest Coverage Ratio (ICR)
(Percent of micro-sized firms with ICR<1)
SI
CZ
HU
PL
SK
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 23
to undertake investments. For micro-enterprises, this dynamic would be compounded by their
still weak financial positions.
16. Empirical analysis supports the hypothesis that the financial crisis altered the
relationship between firms’ financial strength and investment spending. A log-linear form of
a standard firm investment model, as in (Budina et al., 2015; Kalemli-Özcan et al., 2015, Damijan,
2014), is applied to annual observations on a sample of firms from each country. In addition,
potential differences in investment rates related to firm size are also modeled, by running
separate regressions with sub-samples based on firm size (large, medium, and small and micro).
The econometric approach has the following specification:
𝑑𝑙𝑛(𝐼𝑖,𝑡) = 𝛼 + 𝛽1𝐶𝐴𝑆𝑖,𝑡−1 + 𝛽2𝑅𝑂𝐴𝑖,𝑡−1 + 𝛽3𝐷𝐴𝑖,𝑡−1 + 𝛽4𝐷𝐴(𝑝𝑜𝑠𝑡)𝑖,𝑡−1 + 𝛽5𝑂𝑉𝐸𝑅𝑖,𝑡−1
+𝛽6𝑂𝑉𝐸𝑅(𝑝𝑜𝑠𝑡)𝑖,𝑡−1 + 𝛽7𝐴𝑆𝑆𝐸𝑇𝑆𝑖,𝑡−1 + 𝛼𝑖 + 𝛼𝑡 + 𝜀𝑖,𝑡
𝑝𝑜𝑠𝑡𝑖,𝑡−1
= {1 𝑖𝑓 𝑦𝑒𝑎𝑟𝑖,𝑡−1 ≥ 2009
0 𝑖𝑓 𝑑𝑎𝑖,𝑡−1 < 2009
I = Gross investment8
CAS = Cash and cash-equivalents to total assets
ROA = Return on assets (EBITDA/total assets) 9
DA = Financial debt to assets
OVER = EBITDA/Financial debt
post = Indicator that equals one if 𝑦𝑒𝑎𝑟𝑖,𝑡−1 is 2009 or later; zero otherwise
ASSETS = Total assets
αi = Firm (i) fixed effects
αt = Year (t) fixed effects
εi,t = Error term
17. The gross investment rate is modeled as a function of lagged variables that
describe a firm’s liquidity, profitability, and indebtedness. Firm fixed effects absorb all
time-invariant heterogeneity across firms and the year fixed effects control for factors that may
affect investment equally across firms in a country, such as fluctuations in aggregate demand
and interest rates. The debt-to-assets and EBITDA-to-debt variables are interacted with a post-
2008 indicator to examine if the relationship between annual investment spending and firm
indebtedness changed following the 2008 global financial crisis. Total assets are included in
8 Sum of the annual difference of tangible fixed assets and depreciation and amortization. Tangible fixed assets
equals fixed assets less intangible fixed assets, e.g., goodwill.
9 Earnings before interest, depreciation, amortization, and taxes. As in Budina and others (2015) and Kalemi-
Ozcan and others (2015), the model uses the inverse of the indicator for a debt overhang, i.e., EBITDA over
financial debt, as cash flow from operations may be zero or negative.
REPUBLIC OF SLOVENIA
24 INTERNATIONAL MONETARY FUND
sample with all firms to control for differences arising from firm size. This variable is dropped in
regressions on the sub-samples based on firm size. The samples include all firms that have been
active throughout 2006–14.10
18. For Slovenia, bank credit conditions are directly modeled as well. A lagged
explanatory variable (BLS) is added to the model for Slovenia to capture the potential role
changes in bank credit conditions may have played in Slovenian firms’ access to credit for
investment.11 The variable is the diffusion index calculated by the European Central Bank for
Slovenia based on quarterly survey data that is designed to assess bank credit conditions.
19. The repercussions of the global crisis altered the debt/investment relationship. (See
Text Table and Annex II). In periods of high growth and low corporate debt burdens, we would
expect a positive relationship between the ratio of debt-to-assets and the investment rate, as
firms are more likely and able to borrow and invest. We would also expect a negative relationship
between the EBITDA-to-debt ratio and investment, as investment is mainly funded by borrowing
rather than retained earnings. In contrast, with higher debt burdens and low growth, we would
expect the relationship between the debt ratios and investment to reverse, i.e., higher corporate
debt ratios in periods of depressed economic growth constrain investment. The regression
results are in line with these expectations. Post crisis, firms’ investment rates are more sensitive to
their indebtedness and capacity to pay interest relative to the pre-crisis period. In addition,
empirical results indicate that for Slovenian firms, thresholds exist for debt ratios, i.e., a turning-
point level in the ratios’ values above which debt begins to influence investment negatively.
Table 1. Point Estimates of Coefficients on Debt-Related Variables 1/
10 This potentially introduces “survivor” bias in some results. In Slovenia’s case, this may induce an under-
estimation of the magnitude of the effects of leverage on investment, based on the findings of Vodopivec and
Čede (2013). They found that the median leverage increased slightly between 2008 and 2012 for firms with 250+
employees that were still in existence in 2012, while if not accounting for changes in composition, median
leverage decreased for these firms.
11 The European Central Bank diffusion index is only available for Slovenia for the entire sample period.
(1) (2) (3) (4) (5) (6) (7 ) (8) (9) (10)
Pre 2/ Post 2/ Pre 2/ Post 2/ Pre 2/ Post 2/ Pre 2/ Post 2/ Pre 2/ Post 2/
All firms
Debt-to-assets -0.123 -0.578*** -0.202*** -0.100* -0.046 0.067*** -0.039 -0.557*** -0.192** 0.009
EBITDA-to-debt -0.082*** -0.020 -0.098*** 0.142*** -0.002 0.028*** -0.041* -0.026 -0.109*** 0.099***
Credit conditions -0.623***
Small and micro
Debt-to-assets -0.176 -0.668*** -0.246*** -0.131** -0.045 0.065*** -0.156 -0.549*** -0.207** -0.004
EBITDA-to-debt -0.073** -0.015 -0.111*** 0.150*** -0.002 0.028*** -0.054** -0.002 -0.111*** 0.095***
Credit conditions -0.620***
Source: Orbis, IMF staff calculations
1/ Significance level: * p<0.10 **p<0.05 ***p<0.01
Red = Change in coefficient value between pre- to post-crisis periods is consistent with economic theory.
2/ "Pre" refers to observations in the period prior to 2009. "Post" is a linear combination of the coefficients on pre- and post-
crisis observations.
Table. Point estimates of coefficients on debt-related variables 1/
Slovenia Czech Republic Hungary Poland Slovak Republic
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 25
More debt on top of already high debt levels leads to less investment. A higher level
of indebtedness reduces investment by Slovenian and Polish firms post crisis.12 In the
pre-crisis period, the coefficient on debt-to-assets (DA) is negative though insignificant.
Post-crisis the coefficient becomes more negative and significant. In other words, as the
indebtedness of firms in these countries increased, their investment rates fell. For firms in
the other countries, some of which are less indebted, the coefficient on debt-to-assets
turns positive post crisis and in some case is significant. A threshold analysis, described in
Annex III, finds that in Slovenia large firms face a threshold debt-to-asset ratio of close to
76 percent, while the threshold for SME’s is much lower at about 10 percent.
Earnings (and operational cash flow) matter more for investment. The coefficient on
EBITDA-to-debt becomes less negative or turns positive across all countries for the
sample of all firms, and for the sub-sample containing only small and micro firms. This
implies that a firm’s ability or willingness to invest becomes more sensitive to operational
cash flow relative to debt financing. The coefficient on EBITDA-to-debt for the pre-crisis
period is more negative as firms took on more debt to finance investment. This possibly
reflected overly optimistic assumptions by firms and their financiers about the ability of
firms to service their debt burdens. The threshold analysis for Slovenian firms indicates
the level for debt-to-EBITDA at which investment begins to decline is 8 for large firms
and 1 for SMEs (See table).
Table 2. Optimal Debt Thresholds for Slovenia
Profits and cash matter. As expected, more profitable and liquid firms invest more. The
coefficients on cash-to-assets and return on assets are positive and statistically
significant.
12 Replacing the debt-to-assets ratio with an alternative measure of leverage, i.e., debt-to-equity, in the
regression yields qualitatively similar results, though the coefficients are generally smaller. Also the direction of
change on the coefficients for debt-to-equity from the pre- to post-crisis periods for the Czech and Slovak
Republics is consistent with economic theory, whereas using debt-to-assets it is not.
Table. Optimal Debt Thresholds for Slovenia
Debt to assets (DA)
(Percent of assets)
Debt overhang (OVER-1)
(x EBITDA)
Firm sample Optimal Optimal
Large 76 8
SME 10 1
Sources: Orbis; IMF staff calculations
REPUBLIC OF SLOVENIA
26 INTERNATIONAL MONETARY FUND
Table 3. Point Estimates of Coefficients 1/
Tighter bank lending standards lower investment. In all Slovenia-specific estimations,
the coefficient on credit standards was negative, i.e., the investment rate was lower at
higher bank credit standards (see chart), and highly significant. The magnitude of the
impact was somewhat less for smaller firms. This result is consistent with the finding in
Vodopivec and Čede (BoS, 2013) that the majority of firms with 1–15 employees do not
rely on banks for financing.
20. A “back of the envelope”
calculation suggests that the aggregate
debt of small- and micro-sized firms
should be reduced by up to 30 percent
of GDP from 2014 levels. The amount of
further debt reduction depends on the
amount of new equity financing obtained.
With an average debt-to-asset ratio of 56
percent in 2014, medium-, small- and
micro-size firms would need to reduce
debt by about €12 billion, absent new
equity, to lower the ratio to the threshold
level of about 10 percent. New equity
would lower the needed amount of debt reduction. This matters for investment. Based on the
firm-level data from Orbis, SMEs accounted for 60 percent of non-financial corporate investment
over the period 2006–14. With a few exceptions, large firms’ debt-to-asset ratios are under the
relevant threshold.
E. Policy options and conclusions
21. This papers findings indicate that corporate leverage, particularly for SMEs, needs
to be reduced further to accelerate investment. In addition, financial frictions are likely to
remain elevated for some time in Slovenia and central European countries. International
experience suggests possible further measures to restore corporate financial health and get
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
Debt Firms
Debt Exceeding Debt/Assets Threshold, 2014
(Percent of total by firm size)
Large SME
(1) (2) (3) (4) (5)
All firms
Return on assets 1.292*** 1.060*** 0.698*** 2.143*** 0.922***
Cash-to-assets 2.863*** 3.534*** 2.414*** 3.519*** 2.785***
Memorandum: R2 0.230 0.228 0.312 0.306 0.277
Source: Orbis, IMF staff calculations
1/ Significance level: * p<0.10 **p<0.05 ***p<0.01
Table. Point estimates of coefficients 1/
Slovenia Czech Hungary Poland Slovakia
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 27
investment flowing. Below are examples of potential policy interventions, with a focus on
Slovenia, that would be supportive of reducing corporate debt burdens and stimulating financing
for corporate investment:
Closely monitor bank implementation of the NPL guidelines provided by the Bank
Association and the Bank of Slovenia, and adjust these guidelines if needed, based on the
implementation experience.
Consider again the benefits of a centralized privately funded entity (SPV) for SME NPL
resolution. This would quickly reduce the lingering burden NPLs place on bank lending and
stimulate greater economic activity by freeing up productive resources (e.g., blocked
collateral).
Further transfer assets to the Bank Asset Management Company (BAMC), especially claims on
companies that are already part of its portfolio. This would facilitate speedier resolution of
bad debts by mitigating creditor coordination issues.
Explore ways to facilitate equity financing, including mezzanine financing, particularly for
SMEs. Additional equity would provide resources for investment, and improve corporate
leverage ratios enhancing the creditworthiness of corporate borrowers. Mezzanine financing,
which may give the lender the right to convert to an ownership or equity interest in the
company if the loan is not paid back, could help firms gain quicker access to financing for
investment.
Consider sponsoring or supporting regional efforts to develop a market in distressed debt
(IMF, 2015d).
REPUBLIC OF SLOVENIA
28 INTERNATIONAL MONETARY FUND
Figure 3. Slovenia, 2006–14
Sources: Orbis; IMF staff calculations.
-20
-10
0
10
20
30
40
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Investment
(Median, percent change in tangible fixed assets
40
45
50
55
60
65
70
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Debt-to-assets
(Median, percent)
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Return on assets
(Median, percent)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large
Medium
Small
Micro
Cash-to-assets
(Percent, median)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 29
Figure 4. Czech Republic, 2006–14
Sources: Orbis; IMF staff calculations.
-20
-10
0
10
20
30
40
50
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Investment
(Median, percent change in tangible fixed assets)
40
45
50
55
60
65
70
75
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Debt-to-assets
(Median, percent)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Return on assets
(Median, percent)
4.0
6.0
8.0
10.0
12.0
14.0
16.0
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Cash-to-assets
(Median, percent)
REPUBLIC OF SLOVENIA
30 INTERNATIONAL MONETARY FUND
Figure 5. Hungary, 2006–14
Sources: Orbis; IMF staff calculations.
-15
-10
-5
0
5
10
15
20
25
30
35
40
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Investment
(Median, percent change in tangible fixed assets)
25
30
35
40
45
50
55
60
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Debt-to-assets
(Median, percent)
1
2
3
4
5
6
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Return on assets
(Median, percent)
2
4
6
8
10
12
14
16
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Cash-to-assets
(Median, percent)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 31
Figure 6. Poland, 2006–14
Sources: Orbis; IMF staff calculations.
-20
-10
0
10
20
30
40
50
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Investment
(Median, percent change in tangible fixed assets)
44
46
48
50
52
54
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Debt-to-assets
(Median, percent)
0
1
2
3
4
5
6
7
8
9
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Return on assets
(Median, percent)
2
4
6
8
10
12
14
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Cash-to-assets
(Median, percent)
REPUBLIC OF SLOVENIA
32 INTERNATIONAL MONETARY FUND
Figure 7. Slovakia, 2006–14
Sources: Orbis; IMF staff calculations.
-20
-10
0
10
20
30
40
50
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Investment
(Median, percent change in tangible fixed assets)
44
46
48
50
52
54
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Debt-to-assets
(Median, percent)
0
1
2
3
4
5
6
7
8
9
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Return on assets
(Median, percent)
2
4
6
8
10
12
14
2006 2007 2008 2009 2010 2011 2012 2013 2014
Large Medium
Small Micro
Cash-to-assets
(Median, percent)
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 33
References
Banka Slovenije, 2015, “Policy Strategy Paper for Slovenia, 2015,” Ljubljana, Slovenia.
Barkbu, Bergljot, S. Pelin Berkmen, Pavel Lukyantsau, Sergejs Saksonovs, and Hanni Schoelermann,
2015, “Investment in the Euro Area: Why Has it Been Weak,” IMF Working Paper No. 15/32
(Washington: International Monetary Fund).
Bending, Tim, Markus Berndt, Frank Betz, Philippe Brutscher, Oskar Nelvin, Debora Revoltella, Tomas
Slacik and Marcin Wolski, 2014, “Unlocking Lending in Europe,” Working paper, Economics
Department (Luxembourg: European Investment Bank).
Budina, Nina, Sergi Lanau and Petia Topalova, 2015, “The Italian and Spanish Corporate Sectors in
the Aftermath of the Crisis,” IMF Selected Issues Paper, IMF Country Report No. 15/167, pp. 22-48.
Caprirolo, Gonzalo, and Miha Trošt, 2015, “Deleveraging of Non-financial corporations: Taking
stock,” (Forthcoming in Bancni Vestnik)
Christiansen, Lone, and Annette Kyobe, 2014, “Corporate Sector Vulnerabilities,” Selected Issues
Paper, IMF Country Report No. 14/174 (Washington, D.C.: International Monetary Fund).
Damijan, Jože, 2014, “Corporate financial soundness and its impact on firm performance:
Implications for corporate debt restructuring in Slovenia,” EBRD, Working Paper No. 168.
Fazzari, Steven, Glenn Hubbard and Bruce Peterson, 1988, “Financing Constraints and Corporate
Investment,” Brookings Papers on Economic Activity, Vol. 1. pp. 141-95.
EBRD, 2014, Strategy for Slovenia (London: European Bank for Reconstruction and Development).
EBRD, Transition Report 2015-16, 2015, “Rebalancing Finance,” (London: European Bank for
Reconstruction and Development) (Draft).
European Central Bank, 2014, “Deleveraging Patterns in the Euro Area Corporate Sector,” ECB
Monthly Bulletin (February).
Gabrijelčič, Mateja, Uroš Herman, and Andreja Lenarčič, 2015, “Debt Financing and Firm Performance
before and during the Crisis: Micro-Financial Evidence from Slovenia,” Ljubljana, Slovenia.
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34 INTERNATIONAL MONETARY FUND
Annex I. Definitions of Financial Ratios, Variables, and Firm
sizes
CAS Liquidity (Cash and cash-equivalents/Total assets)
DA Debt-to-assets (Financial debt1 /Total assets)
DE Debt-to-equity (Total liabilities/Shareholders’ equity)
EBITDA Earnings before interest, taxes, depreciation, and amortization, i.e., total revenue
less total expenses (excluding interest, taxes, depreciation, and amortization).
ICR Interest coverage ratio (EBITDA/Financial expense)
PRMG Profit margin (Earnings before taxes (EBT)/Operating revenue)
OVER Debt overhang (Financial debt/EBITDA)
ROA Return on assets (EBITDA/Total assets).
Firm Size Classification
(in millions of euros, unless otherwise specified)
1 Financial debt equals long-term debt plus current liabilities. Alternatively, it equals total liabilities less other
non-current liabilities.
Large Medium Small Micro
>= >= >=
Operating revenue 100 10 1
Total assets 200 20 2
Employees (number) 1000 150 15
Other criteria
or
listed
and not
large
and not
large or
medium
All
others
Source: Orbis by Bureau van Dijk
Firm size classification(In millions of euros, unless otherwise specified)
1/ Must match at least one of the conditions to be included in
the size designation.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 35
Annex II. Regression Results by Country and by Firm Size
Dependent variable: Log-difference of tangible fixed assets
SI SI_1 SI_2 SI_3 CZ CZ_1 CZ_2 CZ_3 HU HU_1 HU_2 HU_3
Cash-to-asset 2.863*** 40.350*** 0.455 2.911*** 3.534*** 2.877 3.197*** 3.573*** 2.414*** 1.443 3.958*** 2.399***
0.000 0.000 0.735 0.000 0.000 0.134 0.000 0.000 0.000 0.594 0.000 0.000
Debt-to-assets -0.123 -1.741 0.797 -0.176 -0.202*** -1.091 -0.289 -0.246*** -0.046 -1.176 -0.161 -0.045
0.490 0.435 0.340 0.320 0.005 0.469 0.405 0.001 0.133 0.542 0.702 0.143
Return on assets 1.292*** 18.675*** 6.791*** 1.131*** 1.060*** 5.290** 2.195*** 0.907*** 0.698*** 1.848 1.757*** 0.689***
0.000 0.002 0.000 0.000 0.000 0.021 0.000 0.000 0.000 0.376 0.000 0.000
Debt overhang -0.082*** -4.183** -1.307*** -0.073** -0.098*** -2.213** -0.149 -0.111*** -0.002 -0.196 0.055 -0.002
0.007 0.042 0.000 0.016 0.000 0.014 0.274 0.000 0.679 0.904 0.758 0.753
DA (post) -0.455*** 2.527 0.636 -0.492*** 0.102* 0.578 1.151*** 0.115** 0.113*** -0.889 0.567 0.110***
0.003 0.400 0.373 0.002 0.067 0.674 0.000 0.041 0.000 0.613 0.154 0.000
Overhang (post) 0.062* 2.796* 1.143*** 0.058* 0.240*** 0.600 0.494*** 0.262*** 0.030*** -0.024 0.059 0.030***
0.075 0.078 0.001 0.100 0.000 0.408 0.000 0.000 0.000 0.987 0.754 0.000
Bank survey -0.623*** -0.717*** -0.781*** -0.620***
0.000 0.005 0.000 0.000
r2 0.230 0.167 0.190 0.236 0.228 0.081 0.135 0.251 0.312 0.118 0.226 0.316
F 718.5 4.2 38.5 685.6 2590.5 7.1 192.9 2503.1 10301.3 4.2 150.4 10257.9
N 51,775 345 3,525 47,905 187,665 1,777 24,841 161,047 446,694 808 11,456 434,430
PL PL_1 PL_2 PL_3 SK SK_1 SK_2 SK_3
Cash-to-asset 3.519*** 3.699* 4.008*** 3.463*** 2.785*** -1.423 1.715** 2.815***
0.000 0.052 0.000 0.000 0.000 0.748 0.031 0.000
Debt-to-assets -0.039 0.313 0.389 -0.156 -0.192** 0.158 0.010 -0.207**
0.757 0.759 0.251 0.243 0.030 0.949 0.986 0.021
Return on assets 2.143*** 3.729** 3.479*** 1.843*** 0.922*** -1.916 1.898** 0.894***
0.000 0.017 0.000 0.000 0.000 0.621 0.023 0.000
Debt overhang -0.041* -0.127 -0.055 -0.054** -0.109*** 0.858** -0.321 -0.111***
0.072 0.707 0.569 0.019 0.000 0.047 0.208 0.000
DA (post) -0.519*** -0.612 -1.089*** -0.393*** 0.202** 0.363 0.898 0.203**
0.000 0.532 0.000 0.001 0.010 0.857 0.124 0.010
Overhang (post) 0.015 0.078 -0.057 0.052** 0.207*** 0.493 0.787*** 0.206***
0.516 0.852 0.523 0.025 0.000 0.669 0.007 0.000
r2 0.306 0.149 0.265 0.328 0.277 0.341 0.222 0.281
F 2130.7 19.8 362.6 1812.5 1902.0 12.4 69.2 1850.0
N 110,090 2,226 22,040 85,824 95,647 388 5,429 89,830
Note: All regressions include firm and year fixed effects. Standard errors are clustered at the firm level.
p-values in parentheses: * p<0.10; ** p<0.05; *** p<0.01
1/ First country column is for sample with all firms in that country. Other country-columns: 1=large firms; 2=medium firms; 3=small and micro firms.
Table. Regression results by country and by firm size 1/
Slovenia Czech Republic Hungary
Poland Slovakia
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36 INTERNATIONAL MONETARY FUND
Annex III. Threshold Effects
We tested for the existence of a “turning point” threshold effect between the rate of investment
and the debt–to–assets and debt-to-EBITDA ratios in Slovenia. The turning-point level is the ratio
value above which debt begins to influence investment negatively. The base for the model is the
same as equation (1) in the main text. The primary difference is that the pre- and post-
designations for the variables that reflect a firm’s debt burden have been replaced with a variable
that measures the degree to which a given debt burden varies relative to potential threshold
value. The methodology follows that in Hansen (1999) and Khan and Senhadji (2000).
Immediately below is the specification for the test for a debt-to-assets threshold: 1
𝑑𝑙𝑛(𝐼𝑖,𝑡) = 𝛾1𝑑𝑎𝑖,𝑡−1 + 𝛾2𝑑𝑢𝑚𝑖,𝑡−1 𝐷𝐴∗
(𝑑𝑎𝑖,𝑡−1 − 𝐷𝐴∗) + 𝑋𝑖,𝑡−1𝛽 + 𝛼𝑖 + 𝛼𝑡 + 𝜀𝑖,𝑡
𝑑𝑢𝑚𝑖,𝑡−1 𝐷𝐴∗
= {1 𝑖𝑓 𝑑𝑎𝑖,𝑡−1 ≥ 𝐷𝐴∗
0 𝑖𝑓 𝑑𝑎𝑖,𝑡−1 < 𝐷𝐴∗
da = Debt to assets
X = Vector of control variables (CAS, ROA, OVER)
DA* = Threshold level for debt to assets
dum = Dummy variable that equals one if 𝑑𝑎𝑖,𝑡−1is greater than threshold; zero otherwise
αi = Firm fixed effects
αt = Year fixed effects
εi,t = Error term
The threshold level DA* is chosen so as to minimize the residual sum of squares S(da) with the
threshold level fixed at da.
𝐷𝐴∗ = 𝑎𝑟𝑔𝑚𝑖𝑛𝑑𝑎
{𝑆(𝑑𝑎), 𝑑𝑎 = 𝑑𝑎, … , 𝑑𝑎}, where 𝑑𝑎 = 1 percent and 𝑑𝑎 = 100 percent.
Regressions were run for a sub-sample of large firms and another one for a sub-sample of SMEs,
i.e., all firms not classified as large. In the test for a debt-to-asset threshold, this amounted to one
hundred regressions per sub-sample. The procedure was repeated to test for a threshold level of
EBITDA-to-debt, substituting over-1 for da, with a threshold range of 1 to 10 and an increment of
one. The γs are the coefficients of interest:
𝛾1 is an estimator of the effect of the debt burden on the change in investment for firms
whose debt burden is less than the potential threshold;
1 The specification for the test for a debt-to-EBITDA threshold replaces the regressor 𝑑𝑎𝑖,𝑡−1with 𝑜𝑣𝑒𝑟𝑖,𝑡−1
−1 (i.e.,
debt-to-EBITDA) and threshold variable DA* with OVER-1*.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 37
𝛾2 is the coefficient on the difference between the observed debt burden and the
threshold if the observed debt burden is higher than the potential threshold; and
𝛾1 + 𝛾2 is the impact of the debt burden variable on the change in investment when the
variables value is higher than the potential threshold.
The “turning point” thresholds are identified on the ground of best fit (minimizing the RSS). For
example, the thresholds reported in the table in the main text for debt-to-assets, are the
threshold levels that correspond to the lowest residual sum of squares across the 100 regressions
run for the specified sub-sample of firms. In practical terms, the turning-point threshold implies:
the marginal debt accumulated after debt hits the threshold diminishes investment growth.
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38 INTERNATIONAL MONETARY FUND
ECB QUANTITATIVE EASING: IMPLICATIONS FOR
FISCAL POLICY1
A. Introduction
1. The past few years have witnessed substantial monetary easing by the ECB. With
inflation running well below target, the ECB has been pursuing unconventional monetary policy
easing actions, as the zero lower bound (ZLB) is constraining the use of its short-term interest rate
instruments. The latest round of quantitative easing (QE) implemented since mid-2015 constitutes
the most significant easing episode in the EA to date. The ECB plans to keep this policy in place at
least through March 2017, but has not ruled out extending it beyond that date, and/or broadening
the range of eligible securities, depending on progress on reaching its inflation target.
2. The latest round of QE focuses on the long end of the yield curve. QE is implemented
primarily via purchases of long-term paper of EA sovereigns in the secondary market. The aim is to
bring down long-term interest rates, and thereby boost credit (which remains subdued since the
global financial crisis) and aggregate demand. Other potential transmission channels include the
exchange rate, wealth, and portfolio rebalancing channels.
3. The planned size of QE is substantial, both for the EA as a whole and for Slovenia. With
total planned purchases of government paper in excess of €1 trillion (Table 1), the size of QE is very
large when compared with earlier ECB easing episodes. By end 2015 roughly ⅓ of the planned
purchases had been implemented, with the rest still in the pipeline. Regarding Slovenia, the size of
QE is also quite significant as measured against key debt market metrics: at €4 billion, planned
purchases of Slovenian paper represent some 12 percent of end-2015 public debt and around ⅔ of
the medium- and long-term debt amortization in 2016–17. As with the rest of EA, the bulk of
planned purchases of Slovenian paper remain to be implemented.
4. This paper explores the appropriate response of fiscal policy to QE in the case of
Slovenia. While the monetary aspects of QE, including the relevant channels through which it can
be expected to impact output and prices, have been researched extensively in the literature, the
question of the appropriate fiscal policy response has received much less attention. This is surprising
given that the impact of QE on public debt service costs is in many cases quite significant, and that
fiscal policy can play a role if other transmission channels turn out to be less potent than
expected- particularly in an environment of extensive private sector deleveraging. This paper
explores this issue for the case of Slovenia.
5. By way of preview of the paper’s policy conclusions, empirical and analytical
considerations would argue for a nontrivial fiscal response in Slovenia’s case. While the
temporary nature of QE would argue against deviating from medium-term fiscal targets and the
1 Prepared by Ioannis Halikias.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 39
much-needed consolidation of current expenditure, some front-loading of public investment would
appear justified. This temporary loosening of the primary balance (relative to a counterfactual
without QE) should, however, less than fully offset the lower interest costs, so that the overall
budget deficit and public debt should still decline. Importantly, the success of this strategy is
critically contingent on putting in place a credible medium-term consolidation plan, underpinned by
structural fiscal measures.
B. Fiscal “Windfall” of QE
6. The launch of QE has had a substantial impact on Slovenia’s bond yields. Slovenia’s
bond yields had already been declining steadily since the bank recapitalization at end-2013, with
spreads vis-à-vis core EA countries having narrowed by almost 400 basis points by early 2015; as a
result, Slovenia’s yields had reached Spanish/Italian levels just prior to the launch of QE. Since QE
launch, Slovenia’s yields have declined further, by almost 150 basis points, broadly in line with most
other EA periphery countries (Figure 1). The contribution of lower spreads vis-à-vis the core to this
more recent decline was relatively limited, suggesting that Slovenia’s post-QE trends have been part
of the broader EA-wide picture.
7. Slovenia’s public debt service costs have declined relatively more than those of most
other EA countries. Calculation of the impact of QE on debt service costs relative to a “non-QE”
baseline takes into account sovereign gross financing needs over the QE period (2015-17), but also
prefinancing at lower interest rates for future years, as well as debt buybacks and other debt
management operations. Application of a consistent methodology across EA countries, which
considers the maturity structure of the full range of government securities, suggests that Slovenia’s
QE-related “fiscal windfall” is comparatively large (Figure 2): at 0.5 percent of GDP per year on
average over 2016-17, it is somewhat lower compared to Italy and Ireland, but significantly larger
compared to most other EA countries. These differences reflect less the magnitude of sovereign
yield decline (as Slovenia is not an outlier in this regard), but mainly Slovenia’s comparatively large
near-term rollover needs. In turn, this is a reflection of Slovenia’s market access difficulties since the
global crisis and up to late 2013, which forced large (and expensive) short-term borrowing.
8. The comparatively large QE-related “fiscal windfall” enjoyed by Slovenia renders the
question of appropriate fiscal response policy relevant. The policy question is to what extent
these temporarily lower debt service costs provide room for some primary balance loosening, and to
what extent they should be saved. The remainder of the paper takes up this issue.
C. Fiscal Reaction Functions – Empirical Analysis
9. The question of how fiscal policy tends to respond to borrowing cost shocks has been
the subject of limited recent empirical work. The typical methodology of choice tends to employ
vector autoregressions (VARs) on single-country or cross-country panels – de Groot et. al. (2015)
being a recent reference, although single-equation estimation has also been pursued – see, for
example, Cizkowicz et. al. (2015). A consensus result in the literature seems to be that, in response to
lower borrowing costs, countries tend to loosen their primary fiscal balance, but not by the full
REPUBLIC OF SLOVENIA
40 INTERNATIONAL MONETARY FUND
extent of the decrease in debt service costs; accordingly, the overall balance tends to improve and
the public debt ratio ends up lower after the shock.
10. As a starting point, we estimated a standard VAR to capture historical fiscal reaction
functions to cost of borrowing shocks. Specifically, a 5-variable VAR was employed, including as
endogenous variables the effective interest rate R (defined as interest payments over last period’s
debt stock), the primary balance as a ratio to GDP PB, real GDP growth g, inflation INF, and the
public debt ratio DEBT as a predetermined variable. The system was estimated with one lag (as
indicated by the Akaike criterion), employing a Cholesky identification for the endogenous variables,
on a panel consisting of all 12 EA countries over the full 1998-2015 EMU period. In line with the
recent literature, this was an unconstrained VAR, whereby R is shocked by 1 percentage point, and
is thereafter allowed to evolve endogenously.
11. The empirical results of the unconstrained VAR are on the whole intuitive and in line
with recent work. The estimated impulse responses (Figure 3) suggest that, following a negative
100 basis point R shock, fiscal policy responds by easing, with PB dropping almost 0.3 percentage
points below baseline four years after the shock. Nonetheless, the lower PB offsets the impact of the
R shock on debt service costs only partially, and as a result DEBT falls, remaining some 2 percentage
points below baseline by the end of the five-year horizon. These results are reasonably close to the
literature – e.g. de Groot et. al. (2015). With regard to the other endogenous variables, the impulse
responses confirm a positive impact on INF (after a very brief “price puzzle” period); on the other
hand no significant impact on output growth could be documented (impulse response not shown).
12. While the above results are suggestive, the unconstrained VAR specification may not
adequately capture key design features of QE. Letting R evolve endogenously following the initial
shock yields a path that exhibits limited persistence, with about half the shock gone by year two. By
contrast, QE is planned to remain in place for at least two years.
13. To address these issues, we also estimated a class of constrained VARs, whereby R is
restricted to remain at its initial post-shock level over a two-year period. Beyond year two, the
pace of unwinding QE (and hence the evolution of long-term rates) will presumably hinge on the
response of EA inflation. For the present purposes, two alternative VAR specifications are employed
to capture different possibilities regarding the pace of QE unwinding: A “gradual unwinding”
specification (Constrained VAR I), whereby R is allowed to evolve endogenously after year two; and a
“rapid unwinding” specification (Constrained VAR II), whereby R goes quickly back to baseline by
year three and remains at that level for the remainder of the forecast horizon – this scenario could
correspond to strong inflation response that leads the ECB to quickly reverse its securities purchases.
Other than the constraints imposed on the path of R, the constrained VARs retain the unconstrained
VAR specification.
14. The estimated fiscal response under the “gradual unwinding” specification is
qualitatively similar to the unconstrained VAR, but the size of the effects is somewhat larger.
The impulse responses corresponding to Constrained VAR I (Figure 4) suggest that the PB response
to the R shock is more persistent, with PB falling by 0.35 percentage points below baseline by year
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 41
one, by 0.5 percentage points by year two, and by 0.6 percentage points below baseline by year
three. Thereafter, fiscal policy starts tightening gradually, but PB remains at some ½ percentage
point below baseline through year five. Despite this easier fiscal stance (in primary balance terms),
lower average borrowing costs and (to a smaller extent) higher inflation keep DEBT somewhat lower
relative to the unconstrained VAR throughout the five-year forecast horizon.
15. The profile of the estimated fiscal response under the “rapid unwinding” specification
shows intuitively compelling differences. The impulse responses corresponding to Constrained
VAR II (Figure 5) suggest a distinctly sharper tightening as QE is unwound. Specifically, like in the
gradual unwinding scenario, PB falls by 0.3 percentage points below baseline by year one and by 0.5
percentage points by year two; after that point, however, a much sharper tightening sets in, with PB
getting back to baseline by year four, and actually ending up above baseline by the end of the
forecast horizon. These differences in fiscal response make intuitive sense: Faced with a rapid
unwinding of QE, the fiscal authorities front-load spending to take advantage of low financing costs,
but then tighten sharply to get back to their medium-term baseline as borrowing costs bounce back
rapidly. Finally, the decline in DEBT is more moderate compared to the “gradual unwinding” case,
ending up less than 1 percentage point below baseline by the end of the forecast horizon, as higher
interest cost and lower inflation dominate the tighter PB.
16. Overall, despite these differences, the estimation results under both QE-relevant
specifications paint a qualitatively consistent picture. The following results appear reasonably
robust as regards the fiscal response to lower borrowing costs: the primary balance tends to loosen
upfront while borrowing cost remain low; however, this primary easing does not fully offset the
lower interest bill; as a result, the overall balance improves and the debt ratio falls.
D. Fiscal Reaction Functions – Analytical Considerations
17. While the empirical results discussed above appear reasonably robust, the question
remains whether they can be viewed as optimal policy response in models with optimizing
household and government behavior. Answering this question is important for the purposes of
conducting welfare analysis that can form the basis for extracting plausible policy conclusions. It
turns out that a fairly general class of micro-founded theoretical models, which emphasize
consumption smoothing, can yield results consistent with the empirical patterns documented in the
previous section. By way of illustration, a slightly modified version of the model employed by de
Groot et al. (2015) can help clarify the intuition. The main building blocks of the model are as
follows:
Household intertemporal utility maximization, with households deriving utility both from
private consumption and public good provision – the latter being treated as exogenous and
subject to a stochastic shock;
Household intertemporal budget constraint, with household income consisting of labor
income taxed at an exogenous tax rate, subject to a stochastic shock;
REPUBLIC OF SLOVENIA
42 INTERNATIONAL MONETARY FUND
Interest rate faced by households is an increasing function of total household debt;
Government intertemporal budget constraint, with changes in the expenditure and tax fiscal
instruments subject to (convex) adjustment costs;
Interest rate faced by the government is an increasing function of government debt.
18. By imposing that fiscal authorities have the capacity to commit to a certain deficit
path ex ante, this class of models can generate results consistent with the empirical findings
of the previous section. Commitment capacity allows a (benevolent) government to follow a time-
invariant optimal policy by maximizing household utility subject to resource constraints and
households’ first-order equilibrium conditions. Without going into the details of the requirements
for a closed-form solution, the setup of the model provides intuition with regard to the empirical
findings of the previous section. Higher government expenditure in response to temporarily low
interest rates is a direct implication of optimal household consumption smoothing that includes the
utility provided by higher public spending. Moreover, a temporarily low interest rate today (implying
that it will be higher in the future) leaves the household and government intertemporal budget
constraints unaffected, implying that (noninterest) spending will have to be lower in the future to
revert to their respective steady-state debt paths.
19. The assumption of perfect commitment capacity, however, is in many cases a strong
one. The perfect commitment assumption rules out time inconsistency, which would lead the fiscal
authority to re-optimize each period; in such an environment, it is clear that a time-invariant optimal
policy cannot be supported. Yet there is strong evidence that time inconsistency problems are
pervasive in policymaking, with present bias for public spending in the case of fiscal policy having
received considerable attention in the literature. Wide concern about these issues is evidenced by
the perceived need to adopt a variety of commitment mechanisms, such as fiscal rules in the case of
fiscal policy.
20. In the presence of time inconsistency suitable extensions of the model can restore the
analytical basis of the empirical results. To address these problems, the simple model described
above can be extended to incorporate endogenous commitment mechanisms which can support an
optimal tradeoff between the government’s committing not to overspend against its desire for
flexibility to respond to privately observed shocks to the social value of spending. Extending the
model along these lines, as suggested by Halac and Yared (2014, 2015), which in turn builds on
commitment mechanisms derived by Amador et al. (2006) in a more general context, would be a
relatively straightforward option. Such an extension would add a history-dependent dimension to
the optimal rule to support commitment, but would otherwise leave intact most of the key features
of the simpler model.2 While the extended model’s additional complexity would typically not allow
closed-form solutions, a wide range of calibrated parameter values validates the main empirical
results of the previous section: a temporary increase in primary expenditure in response to a
2 An extension along the lines of Halac and Yared (2015), in particular, also provides a welfare assessment of
decentralized versus centralized (e.g. SGP-type) fiscal rules which is relevant for EA countries.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 43
temporary decline in interest rates, which however does not fully offset the savings in debt service
costs, with debt eventually returning to its original steady state level.
E. Policy Implications and Additional Constraints
21. The empirical results and theoretical discussion of the previous sections provides a
basis that can underpin policy recommendations on the appropriate fiscal response to QE in
Slovenia’s case. Given the temporary nature of QE, there is little ground to deviate from a medium-
term objective for the primary balance that safeguards public debt sustainability.3 At the same time,
there appears to be scope for some flexibility in the near term that would allow for a transitory
increase in public good provision. In terms of quantifying this extra room, the VAR results under the
QE-relevant specifications suggest that roughly half of the QE-related interest savings could be
spent over a two year horizon; in conjunction with the estimated “fiscal windfall” for Slovenia (Figure
2), this would imply room to raise primary spending by some ¼ percentage points of GDP each year
during 2016-17.
22. Public investment appears to be the best candidate to make use of this extra primary
spending space. Given the need to adhere to the medium-term fiscal targets, any up-front
spending increase would need to be reversed in later years – this would seem to rule out spending
categories such a the wage bill, transfers, and, possibly, spending on goods and services, as
increases in these categories tend to be politically difficult to scale back. By contrast, public
investment (either the EU-funded or the domestic component) is probably best suited to make use
of the temporary spending room, given that it is typically part of a multi-year spending plan, and as
such its reallocation across different periods should be easier. An additional argument for choosing
public investment is that it could help offset any underperformance in private investment relative to
the baseline in the near term, whose projected rebound is key for sustaining the recovery.
23. In determining the scope for additional, front-loaded primary spending, a number of
potential additional constraints also need to be taken into account. These constraints could not
be adequately incorporated in the empirical and analytical models considered above:
Consistency with the SGP: Any near-term stimulus would need to be consistent with SGP
requirements, notably the constraint of not breaching the 3 percent deficit limit. Assuming
that Slovenia adheres to its 2016-17 budget targets, the temporary expansion suggested
above would leave it comfortably below this threshold. While the precautionary arm of the
EDP also prescribes a minimum annual structural fiscal adjustment, adequate safeguards
that Slovenia will adhere to its medium-term fiscal targets, including importantly through
structural fiscal reforms of current spending that strengthen credibility (see also below),
should allow some flexibility in this regard.
Implementation capacity: Availability of public investment projects with high rates of
return and administrative capacity to adequately manage them could also be a potential
3 On Slovenia’s medium-term fiscal strategy, see Government of the Republic of Slovenia (2015).
REPUBLIC OF SLOVENIA
44 INTERNATIONAL MONETARY FUND
constraint. Calibrating this constraint at the average of the three highest levels of public
investment (in terms of GDP) reached over the previous 10-year period suggests that the
envisaged temporary public investment increase should be attainable.
Avoiding fiscal procyclicality: The models discussed above do not adequately capture
cyclical considerations, but it would arguably not be desirable to be providing extra stimulus
if it threatens to move the economy significantly above potential. IMF staff estimates of a
still negative output gap, together with absence of inflationary pressures and a large current
account surplus provide assurance in this regard.
24. It should be emphasized that the suggested strategy of a front-loaded primary
spending expansion hinges crucially on a credible medium-term fiscal consolidation strategy.
Indeed, it is essential that higher primary spending in the near term not be perceived as signaling
that Slovenia’s medium-term fiscal targets could be compromised, as such credibility costs could
more than offset any benefit deriving from the suggested near-term strategy. Accordingly, it is
essential that any near-term stimulus should be pursued in the context of developing a credible
consolidation strategy––underpinned by an adequate institutional framework––that provides strong
assurances that fiscal sustainability will be maintained. In this sense, the envisaged near-term
flexibility can be viewed as a benefit of medium-term credibility.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 45
Figure 1. 10-Year Government Bond Yield and Spread vs. Germany
Figure 2. Selected Euro Area Countries: Estimates of Funding Cost Savings From QE, 2016–17
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Selected Euro Area Countries: Estimates of Funding Cost Savings From QE, 2016-17
(Annual average, percent of GDP)
Sources: Bloomberg, ECB, and IMF staff calculations.
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
2011 2012 2013 2014 2015 2016
Spread
Yield
Figure 1: 10-Year Government Bond Yield and Spread vs. Germany
(percent)
Source: Bloomberg.
QE launch
REPUBLIC OF SLOVENIA
46 INTERNATIONAL MONETARY FUND
Figure 3. Impulse Responses-Unconstrained VAR
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
1 3 5 7 9 11
R => R
Figure 3. Implulse Responses -Unconstrained VAR
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
1 3 5 7 9 11
R=>INF
-0.4
-0.3
-0.2
-0.1
0
1 3 5 7 9 11
R=>PB
-3
-2
-1
0
1 3 5 7 9 11
R=>DEBT
Source: IMF staff calculations.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 47
Figure 4. Impulse Responses-Constrained VAR
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
1 3 5 7 9 11
R=>R
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
1 3 5 7 9 11
R=>PB
Figure 4. Impulse Responses - Constrained VAR I
Source: IMF staff calculations.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1 3 5 7 9 11
R=>INF
-3
-2
-1
0
1 3 5 7 9 11
R=>DEBT
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48 INTERNATIONAL MONETARY FUND
Figure 5. Impulse Responses-Constrained VAR II
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
1 3 5 7 9 11
R=>R
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1 3 5 7 9 11
R=>PB
Figure 5. Impulse Responses - Constrained VAR II
Source: IMF staff calculations.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1 3 5 7 9 11
R=>INF
-3
-2
-1
0
1 3 5 7 9 11
R=>DEBT
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 49
Table 1. Eurosystem Sovereign Debt Purchases under PSPP, ex Supranational Debt
(as of Nov. 30, 2015)
42369 Cum.
ITA 414.3467 515.0072 155.692 193.5154 8.876 72.03 37.2208068 16.2846 15.7074 0.57715 9.28
FRA 393.9825 503.9139 174.262 222.8851 10.221 83.5 37.4632499 18.8782 18.0913 0.78692 7.81
DEU 326.2329 407.5836 226.437 282.9024 12.903 105.2 37.179605 23.7803 22.9629 0.81739 7.02
ESP 206.8117 260.5141 110.324 138.9715 6.334 51.68 37.1874783 11.6842 11.2802 0.40401 9.74
NLD 87.06892 108.2291 50.6277 62.93165 2.883 23.42 37.2165665 5.29518 5.10809 0.18709 6.59
BEL 91.11958 120.2224 29.5204 38.94893 1.764 14.45 37.0998626 3.26695 3.16144 0.10551 9.62
AUT 52.29325 66.7427 24.1776 30.85828 1.411 11.49 37.221776 2.59683 2.50473 0.0921 8.25
PRT 30.04083 36.60148 22.4926 27.40478 1.248 10.2 37.2270807 2.30654 2.22441 0.08212 10.57
IRL 35.62075 45.84849 15.3439 19.74955 0.84 6.899 34.9324398 1.55977 1.60305 -0.0433 9.34
FIN 24.75067 28.93388 15.6074 18.24523 0.909 7.352 40.2954692 1.66219 1.48094 0.18125 7.61
SVK 9.9 12.6 7.4699 9.507143 0.533 4.345 45.7024793 0.98235 0.98214 0.0002 8.54
SVN 3.3 3.85 4.04082 4.714286 0.248 2.031 43.0818182 0.45918 0.44643 0.01275 8.13
Others 5/ 25.38015 31.95856 7.45911 9.392468 0.167 2.342 24.9348722 0.5295 1.25375 -0.7243 6.03
Euro Area 1700.848 2142.005 843.454 1060.027 48.337 394.9 37.2554747 89.2857 86.8067 8.06
Overall
eligible
market
(s.t. limits,
nominal) 1/
Overall
eligible
market
(s.t. limits,
market) 1/
Planned purchases
(EUR billion)*Actual purchases
(EUR billion,
market) In percent
of target
(market)
In
percent
of PSPP
4/
Sources: Bloomberg L.P., ECB, and IMF staff calculations. Note: *assuming monthly purchase volume of EUR44 bln.; 1/ including estimated net
issuance until end-2016 and current SMP holdings (where applicable) and inclusion of eligible sub-national debt, after application of security
issue and issuer limits; 2/ excluding 12% of supranational debt purchases; 3/ partially consistent refers to the maximum amount determined as
the lesser of the capital key-based allocation and the still available stock of eligible government debt (e.g., for Germany the latter is binding); 4/
excluding Greece and Cyprus, adjusted for supranational purchases; 5/ "Others" includes Estonia, Latvia, Lithuania, Luxembourg, Malta,
Slovenia, and the Slovak Republic.
Target
(nominal)
Target
(market)
ECB
capital
key 3/
Dev.
from
capital
key
Avg.
maturity
(years)
REPUBLIC OF SLOVENIA
50 INTERNATIONAL MONETARY FUND
References
Amador, M., I. Werning, and G.-M. Angeletos, 2006, “Commitment vs. Flexibility,” Econometrica, Vol.
74, No. 2, March.
Cizkowicz, P., A. Rzonca, and R. Trzeciakowski, 2015, “Windfall of Low Interest Payments and Fiscal
Sustainability in the Euro Area: Analysis through Panel Fiscal Reaction Functions,” Kyklos, Vol. 68, No.
4, November.
de Groot, O., F. Holm-Hadulla, and N. Leiner-Killinger, 2015, “Cost of Borrowing Shocks and Fiscal
Adjustment,” Journal of International Money and Finance, Vol. 59.
Government of the Republic of Slovenia, 2015, “Stability Programme – Amendments 2015,” April.
Halac, M. and P. Yared, 2014, “Fiscal Rules and Discretion Under Persistent Shocks,” Econometrica,
Vol. 82, No. 5, September.
Halac, M. and P. Yared, 2015, “Fiscal Rules and Discretion in a World Economy,” Columbia Business
School Working Paper No. 15-72, September.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 51
FINANCIAL SECTOR DEVELOPMENT ISSUES AND
PROSPECTS1
A. Introduction
1. The global financial crisis has raised legitimate questions about the possibility that
stability and growth could be hurt by too much finance. Recent research on financial
development has advanced the discussion, including analyses by the International Monetary Fund
(IMF), the Bank for International Settlements (BIS), and the Organization for Economic Cooperation
and Development (EOCD), and in academia.
2. In Slovenia, the global crisis and the more recent domestic banking crisis exposed
weaknesses in the banking sector. The ongoing balance sheet deleveraging process that followed
the remarkable boom-bust cycle since the country’s EU accession (2004), and the nonperforming
asset overhang has led to a sustained contraction in credit to the economy, and in particular to
SMEs, since the third quarter of 2011. To this can be added the reduced demand for credit by
corporates reflecting impaired balance sheets and the economic contraction. Despite the
considerable deleveraging process, Slovenia still compares relatively well to other peer countries in
terms of credit-to-GDP ratio, the main traditional indicator of financial development. However, the
latter is only one metric to assess financial development.
3. Using a new broad measure of financial development developed by IMF staff, this note
assesses the implications of Slovenia’s financial development for economic activity and
financial stability.2 The analysis suggests potential ways the financial sector could advance while
minimizing the negative effects that financial deepening had in Slovenia. That is, the lower quality of
financing led to a build-up of risks and resulted in high NPLs and a misallocation of resources. Key
elements to reduce financial sector vulnerabilities would include better regulation and supervision
(including ensuring adequate governance), further development of financial markets, and improved
efficiency of financial institutions.
4. This note is structured as follows. Section II discusses the structure of the financial system
and the developments since the 2012–13 banking crisis. Section III presents some features of the
regulatory and supervisory framework. Section IV introduces a broad indicator of financial
development that take into account the depth, access, and efficiency of financial institutions and
markets. On this basis, it assesses Slovenia’s standing in a cross-country context. Finally, section V
concludes with some potential avenues for future financial sector development.
1 Prepared by Claudio Visconti (MCM).
2 Sahay, Ratna and others, 2015, “Rethinking Financial Deepening: Stability and Growth in Emerging Markets,” IMF,
Staff Discussion Note No. 15/8.
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52 INTERNATIONAL MONETARY FUND
B. Financial System Structure and Recent Developments
System Structure
5. Slovenia’s financial system has assets of about 150 percent of GDP and is bank
centered. Banks account for about 70 percent of assets with the remaining roughly equally split
between insurers and a group comprising pension companies and funds, investment funds, and
leasing companies. Bank ownership is concentrated in the state (63 percent of the total sector’s
equity) while other domestic entities control about 7 percent of the sector, and non-residents about
30 percent. Market share is also concentrated with the largest domestic banks controlling about
57 percent of the sector’s assets, small domestic banks 8 percent, and banks under majority foreign
ownership 35 percent.3
Developments in Recent Years
6. The global financial crisis brought a sudden stop of capital inflows and associated
credit boom. Reflecting lost access to external funding, the foreign to total liabilities ratio of
Slovenian banks fell to 13 percent by September 2015 down from a 40 percent peak in June 2008.
The squeeze in funding sources forced banks into a pronounced deleveraging with a dramatic cut in
lending that reinforced the recession. As a result, NPLs (mainly from corporates) increased sharply
peaking at 14.4 percent in 2012 from 3.8 percent in 2008, impairing the balance sheets of banks and
corporates in a protracted process. The difficulties faced by corporates led to a rapid deterioration in
the quality of banks’ portfolios imposing operating losses that reduced banks’ capital and increased
solvency risks. In 2013 a comprehensive asset quality review of eight banks determined that foreign
banks had a capital shortfall of 78 percent relative to the capital levels reported in September 2013
while banks under domestic ownership showed a shortfall of 244 percent.4
7. State banks were recapitalized at a total cost of 10 percent of GDP. As part of the
measures to stabilize the banking sector, six banks received capital injections in 2013–14. In
addition, the establishment of the Bank Asset Management Company (BAMC) in late 2012 allowed
the transfer of 53 percent of the EUR 9.4 billion in NPLs from five of the recapitalized government
banks by end-2014, primarily involving loans to large state-owned corporates.5 The remaining NPLs
are concentrated (over 70 percent) at small and medium-sized enterprises (SMEs). The crisis exposed
inadequate bank governance and risk management practices that allowed endemic connected
lending and lax risk controls, especially for state-owned banks. Likewise, significant weaknesses in
corporate management and governance (including through state ownership) were also exposed.
3 The sale of NKBM to a U.S. equity fund was announced in June 2015. It is the second largest bank with a 12 percent
share in total bank assets and deposits and a 9 percent share in total loans. In comparison, NLB, the largest bank, has
shares of 32, 34 and 30 percent, respectively.
4 Report of the Bank of Slovenia on the Causes of the Capital Shortfalls of Banks, March 2015.
5 Over 70 percent of claims against large corporates were transferred to BAMC. Some claims less than 90 days in
arrears were also transferred.
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INTERNATIONAL MONETARY FUND 53
8. In addition, other measures were adopted to support the effort of stabilizing the
banking sector. Besides the recapitalization and transfers of NPLs to BAMC, the authorities
implemented: (i) Corporate Insolvency Framework: the 2013 reform opened more options to help
address corporate debt overhang, including voluntary multilateral restructuring agreements (MRAs).
Besides banks, BAMC also implements MRAs. (ii) Supervisory Actions: to monitor and support
restructuring of NPLs the BOS established reporting requirements, including 3-year management
plan and restructuring strategy, asset reclassification, release of impairment provisions. Despite all
these actions, most MRAs involve debt re-profiling, but not debt reduction, new financing, nor
recapitalization.
9. Balance sheets of both banks and corporates remain impaired with the repair process
proceeding very slowly. System wide NPLs were at 10.3 percent (EUR 3.7 billion, of which
corporates represent 60 percent) in November 2015 and overall capital ratios were at 20.5 percent
(CAR) and 19.8 percent (core tier 1) by September 2015. However, credit to the private sector is still
contracting by 5.7 percent total and 10.2 percent to nonfinancial corporates (yoy) in December 2015
and the income generation capacity of banks is limited. Bank profitability is still low, with ROE at
6.1 percent in September, after highly negative readings since 2010 and net interest margin of
2.2 percent. It is thus important to accelerate the process of balance sheet repair and NPL resolution.
C. Regulatory and Supervisory Framework
10. The last assessment of Slovenia’s regulatory and supervisory frameworks was in 2012.
It was conducted by the IMF and the World Bank in the context of the Financial Sector Assessment
Program (FSAP).6 The assessment noted that state-controlled banks should be privatized, which
“would help address the long-standing governance weaknesses of these banks, which were put into
the spotlight by the crisis. Reducing government influence on the day-to-day operations and
lending decisions of banks will help improve the risk management practices and the efficiency and
stability of the banking system over the longer term.”
11. In addition, the assessment of the Basel Core Principles for Effective Banking
Supervision noted weaknesses in banking supervision. In particular, it found that the BOS powers
should be strengthened in several areas, including: (i) the licensing or removal of bank supervisory
board members; (ii) the requirement for banks to obtain authorization for acquiring non-bank
financial companies; (iii) the power to direct banks to increase capital (without shareholders’
discretion to impede BOS’s requirement); (iv) the lack of granularity in the reporting of problem
assets; and (v) the relatively low provisioning of NPLs.
12. The BOS legal framework was strengthened with the approval of amendments to the
Banking Law, in line with the FSAP recommendations. The most significant changes in the 2012
amendment provided the BOS with: (i) increased powers in relation to banks’ supervisory board
members; (ii) broader powers towards effective implementation of financial stability measures over
6 Republic of Slovenia: Financial System Stability Assessment, IMF Country Report No. 12/325.
REPUBLIC OF SLOVENIA
54 INTERNATIONAL MONETARY FUND
banks and the banking system (extraordinary measures include the appointment of extraordinary
administration, compulsory disposal of shares of existing shareholders, capital increase, and transfer
of assets and liabilities of the bank); (iii) power to authorize qualifying investment of banks in other
financial undertakings; (iv) increased powers over the execution of the supervisory measures,
especially in relation to requirements for recapitalization of banks; and (v) legal protection of
supervisors.
13. The revised Banking law transposes to national legislation the European directives on
capital requirements (CRD IV). It was approved in March 2015 and includes the frameworks for
capital buffers and for early intervention, besides the recovery of credit institutions and investment
firms (part of BRRD).7 The Single Supervisory Mechanism (SSM) framework will strengthen
supervision, aligning standards with European best practices. In particular, it seeks to ensure equal
supervisory quality and treatment between the group of “important banks” (direct ECB supervision)
and that of “less important banks” (indirect ECB supervision, direct supervision by national
competent authorities), including by applying common rules and procedures from the single
supervision manual. Improved supervision should also address weak bank risk management and
governance, and monitor connected lending. Other European initiatives, such as the establishment
of a macroprudential framework and of a credit register, once fully operational will reinforce
Slovenia’s regulatory and supervisory frameworks.
D. Indicators of Financial Development
14. The most traditional indicator of financial development is size. It is traditionally
measured by the ratio of non-financial private sector credit (usually from banks) to GDP. However, in
light of its simplicity, it does not capture other important aspects of financial development. These
include the liquidity of markets, other sources of credit (from nonbanks), access to financial services,
and efficiency in the delivery of such services.
15. A new and broad measure (Financial Development Index or FD index) was developed
by IMF staff. To better capture the different characteristics of financial development the FD index
covers both institutions and markets and includes metrics for depth (size and liquidity of markets),
access (access to financial services by individuals and companies), and efficiency (provision of
financial services at low cost and sustainable revenues, and the level of activity of capital markets)
(Box 1).
16. The IMF analysis confirms a number of important financial development features.
Based on data for 176 countries, not only does it demonstrate the non-monotonic effect (marginal
return) of financial development on growth and stability, but it also shows that, as economies evolve
and the process of financial development advances, the relative benefits from institutions decline
and those from markets increase. The intuition behind this result is that too much finance (larger
financial systems) and/or fast financial deepening tend to contribute to real output losses through
7 The remaining BRRD provisions are planned to be transposed into national legislation by April 2016, in the context
of the Law on Banking Resolution.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 55
more frequent booms and busts and greater financial instability. These potential trade-offs, with
costs outweighing benefits at some stage, are the underlying factors behind the bell-shaped
relationship between financial development and growth (Box 2).
17. The cross-country evidence highlights the importance of strong regulatory and
supervisory frameworks in promoting financial stability and financial development. It shows
that the same subset of regulatory principles is critical for both, and that there are concrete
regulatory actions that would promote financial development and stability simultaneously.8 That
said, good regulation must be complemented by adequate and efficient supervision and oversight
so as to produce the expected results. Relatedly, it also suggests that the faster the pace of financial
development the higher the risks to economic and financial instability, likely due to the fact that
regulation and, particularly, supervision would only follow with a lag.
Financial development in Slovenia compared to the international experience
18. Credit in Slovenia grew at a rapid pace doubling in 2004–10. So, based on the private
sector credit-to-GDP metric, Slovenia was ahead of most CEE countries and some other emerging
market economies (EMEs) in 2013 (Figure 1).9
19. A more complete picture, however, emerges through the use of the broad FD index.
The annual evolution of the financial development index in the period 2004–2013 (since
Slovenia’s EU entry), shows Slovenia below the average for the EA, but above CEE and EME
(Figure 2). It also shows that the narrowing gap between Slovenia and the EA was reversed from
2009. This pattern also coincides with the index for Slovenia moving towards the average for the
CEE and EME.
However, the sub-indices, financial institutions (FI) and financial markets (FM), point to a
stronger position of institutions, comparable to that of the average EA, and a weaker position in
markets (Figure 2). The latter puts Slovenia closer to the average CEE and EME. More developed
institutions than markets is a feature shared by the three comparator groups.
The components of the sub-indices, depth, access, and efficiency for each institutions and
markets suggest that Slovenia’s position is driven by relatively strong access levels of both
institutions and markets, and weaker depth and efficiency (Figure 3). And the latter is much
lower for markets than for institutions.
The variables underlying the sub-indices, particularly those related to market depth (FMD) and
efficiency (FME), reveal that the deficiencies are driven by very low relative levels of nonbank
8 These principles capture: (1) the ability of regulators to set and demand adjustments to capital, loan loss
provisioning, and employee compensation; (2) regulatory definitions, such as definitions of capital, nonperforming
loans, and loan losses; and (3) financial reporting and disclosures.
9 CEE countries comprise, besides Slovenia: Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, and Slovak Republic.
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56 INTERNATIONAL MONETARY FUND
market development, such as stock market capitalization and trading volumes (both measured
as shares to GDP), number of debt securities issuers, and stock market turnover ratio (Figure 4).
Regarding institutions, the components of depth, such as assets of pension and mutual funds,
for Slovenia lag behind those for the EA (for both) and EME/CEE (for pension fund assets), while
mutual fund assets are well below the EA and similar to the average in EME/CEE.
The evolution of Slovenia’s financial development in 1995–2013 has been skewed towards
increased relevance of financial institutions along with a decrease in the role of markets. This
contrasts with a more balanced process in the EA and even for EME, with the importance of both
markets and institutions increasing over time, albeit relatively more for the latter (Figure 5).
Based on the estimations presented in the Sahay and others (2015), Slovenia would lie close to
the turning point at which the positive effects of financial development on growth and on
growth volatility begin to decline (Figure 6; see next paragraph for specifics on Slovenia).10 And
past the point at which the effect of continued financial deepening of institutions on financial
stability is increasingly more negative.11
E. Future Prospects
20. The results suggest some potential ways the financial sector could advance in Slovenia
while minimizing the negative effects of too much finance. The analysis indicates that financial
deepening has led to low-quality financing, with the build-up of risks, vulnerabilities, and declining
efficiency of investment. As a consequence, the current bank assets are not productive enough, with
significant misallocation of loans and high NPLs. Expanded credit would only help support higher
sustainable growth if applied efficiently to productive activities by viable corporates. Conversely, and
as the recent experience in Slovenia demonstrates, higher/faster credit could lead to increased
vulnerabilities, a boom and bust cycle, and ultimately lower output.
Better regulation and supervision
21. Adequate regulation and supervision are key elements in a balanced process of
financial development preserving economic and financial stability. As demonstrated by
Slovenia’s own recent experience, when institutional deepening advances too fast it tends to lead to
economic and financial instability. Reasons often associated with this result are incentives towards
10 This result has to be taken with caution since, as explained in IMF 2015, “there is no one particular point of “too
much finance” that holds for all countries at all times. The shape and the location of the bell may differ across
countries depending on country characteristics including income levels, institutions, and regulatory and supervisory
quality”. The estimated turning point for which the positive effects of financial development on growth begin to
decline is on average in the 0.4 and 0.7 range with a confidence level of 95 percent. This wide band around the
turning point reflects differences in fundamentals and institutional settings for the countries in the sample. As such, a
country to the right of the range may still be at its optimum if it has above average quality of regulations and
supervision while a country to the left of the range with weak institutions may have reached its maximum already.
11 Financial stability is measured here by distance to distress, which in turn is defined as the sum of the capital-to-
assets ratio and the return-on-assets (ROA) ratio, divided by the standard deviation of the ROA.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 57
risk-taking and over-leveraging, particularly when institutional governance is lagging and regulation
and supervision are not adequately developed and/or enforced. Importantly, the empirical results
suggest that effective implementation of key regulatory principles could help shift the turning point
region for which the positive effects of financial development on growth, and growth and financial
volatility begin to decline. The experience in Slovenia, and elsewhere, points to the large output loss
stemming from less-than-desirable financial regulation and/or supervision. The excessive credit
growth before the crisis is now reflected in impaired sectoral balance sheets and high NPLs with
consequent low credit provision to the economy. This is fully in line with the argument that too
much finance increases the frequency of booms and busts and leaves countries ultimately worse off
and with lower real GDP growth.
Further financial market development
22. Relative to the international experience, Slovenia is in the middle range in terms of the
financial development. It fares particularly well in terms of financial institutions while market
development lags behind, showing much room for improvement. This suggests that a process more
geared towards markets than institutions and focusing on increased access and efficiency could
allow greater benefits from further financial development in terms of economic and financial
stability.
23. Given the size of Slovenia’s domestic market and its increasing integration with the
European market, developing a vibrant local capital market is likely to be difficult. However,
corporates, and in particular SMEs, would benefit from more options for equity financing rather than
just bank debt funding. In this context, further development of local nonbank financial institutions
and/or the capital market could facilitate new investments by the sector. A shift in the structure of
corporate financing towards a higher proportion of equity would also increase corporate resilience
to shocks. For instance, incentives towards equity could be supported by easier foreign investment
and ownership and a more dynamic privatization process.
More efficient financial institutions
24. Although Slovenia’s financial system is bank centered, it could benefit from stronger
and efficient nonbank institutions, such as pension and mutual funds. These could be an
additional source of corporate finance as they currently play a very small role in the financial sector.
However, challenges to this end remain, given: (i) the small size and low turnover ratio of the
domestic stock market; (ii) the apparent lack of interest to create new mutual funds with focus on
domestic investments; (iii) the trend in last few years towards consolidation of mutual funds with a
wider global/regional investment strategy; and (iv) the strategy of pension funds to invest abroad.
Potential Implications for the Banking Sector
25. Banks face limitations to the traditional role of credit providers. The process of balance
sheet repair is still unfolding while high NPLs reduce profitability and keep lending standards tight
(not necessarily a bad outcome). From the asset side, credit to the private sector continues to
REPUBLIC OF SLOVENIA
58 INTERNATIONAL MONETARY FUND
contract and the loan-to-deposit ratio to fall. On the liability side funding is restricted, including the
fall on deposits with agreed maturity since early 2013 (by over 25 percent). Moreover, as
deleveraging continues, the share of foreign in total liabilities have been reduced to below
13 percent from the 40 percent peak in mid-2008 without an offsetting increase in other sources of
funds.
26. Given these factors and the relatively undeveloped capital market, a migration of the
demand for credit to nonbanks would be a possibility. The higher competition, in turn, would
tend to reduce the space for bank activities and profits. For instance, the net value of commercial
paper by companies (a short-term funding instrument) issued in the domestic market has been
increasing continuously since 2011, albeit
from a very low level, to EUR 230 million in
2014 from EUR 9 million in 2011. In the
same period the net value of outstanding
corporate bonds increased to
EUR 140 million from EUR 65 million,
recovering from a sharp decline in 2012–
13. Despite these increases, commercial
paper and bonds represented 1.0 percent
and 14 percent, respectively, of the
Ljubljana Stock Exchange market turnover
in 2015 (chart). This suggests the potential
for the expansion of these financial
instruments in the local market.
27. Banks need to improve efficiency and reduce costs. Banks’ low efficiency and profitability
suggest that some consolidation in the sector could be beneficial. Of the 16 commercial banks
operating in Slovenia, the top five control close to 70 percent of the assets while the assets of the
10 smallest banks represent 25 percent of the total.12 Privatization of public banks could also
generate efficiency gains if followed by active supervision and regulation. To resume their lending
function banks need to accelerate the process of balance sheet repair and NPL resolution. Without
higher efficiency and with it competitive lending rates, banks will tend to lose share in credit
provision, particularly to corporates. And small banks, with less opportunity for economies of scale
and diversification, are more likely to be vulnerable to various shocks.13
12 These numbers reflect the merger of Abanka Vipa and Banka Celje.
13 On the other hand, the authorities should be careful not to create too big to fail institutions.
84.9
14.2 0.9
LJSE 2015 Turnover(In percent)
Shares
Bonds
Com Paper
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 59
28. Banks need to offer
flexible conditions to borrowers
with access to external funding.
A process of market segmentation
could result with the best
corporate credits potentially
moving to borrow from abroad or
even from nonbanks, while banks
would tend to keep the lower-tier
credit risks. In fact, there is
evidence that some process of
disintermediation of banks is
underway. For instance, domestic
loans to corporates continued to
shrink in 2015 (yoy), albeit at a
lower rate.14 In contrast, the international net financial position of corporates doubled to 28 percent
of GDP in the period 2008–14 and increased by 10 percentage points in 2011–14.15 Cross-border
financing to corporates increased in 2014 with the issuance of equity and debt securities, mostly to
the EU. Moreover, nonbank financing to corporates, including SMEs, is on the rise in Europe,
although from a relatively low level. Initiatives to improve funding to SMEs have taken various forms
such as publicly tradable debt (mini bonds), equity, private-debt funds, private placements (placing
securities privately with institutional investors), and direct borrowing from nonbanks.
14 The yoy growth rate has been negative since early 2011.
15 Nominal GDP is estimated as flat in 2008–14 and to have grown by 1 percent in 2011–14.
-100
-80
-60
-40
-20
0
20
-10
-8
-6
-4
-2
0
2
2006 2007 2008 2009 2010 2011 2012 2013 2014
ROAA ROAE (rhs)
Bank Profitability
(In percent)
Source: Bankscope
0
50
100
150
200
250
-120
-100
-80
-60
-40
-20
0
20
2006 2007 2008 2009 2010 2011 2012 2013 2014
ROAA
ROAE
cost/income (rhs)
Bank Profitability and Efficiency
(In percent)
Source: Bankscope
-40
-30
-20
-10
0
10
20
30
40
-40
-30
-20
-10
0
10
20
30
40
Jan
-08
Jun
-08
No
v-0
8
Ap
r-09
Sep
-09
Feb
-10
Jul-
10
Dec-
10
May-1
1
Oct
-11
Mar-
12
Au
g-1
2
Jan
-13
Jun
-13
No
v-1
3
Ap
r-14
Sep
-14
Feb
-15
Jul-
15
Credit to Private Sector(y-o-y percentage change)
Households
Total
Non-financial corporations
Sources: BOS
REPUBLIC OF SLOVENIA
60 INTERNATIONAL MONETARY FUND
29. Banks also face some limits to expand credit from the asset side. State-aid rules in the
context of the bank recapitalization process impose some limitations on sectoral lending and on
minimum required rates of return (return on equity) for project lending.16 In addition, the higher
European regulatory capital requirement with additional capital buffers to mitigate cyclical or
structural systemic risks represent an increase to banks’ cost of funding, as more and better quality
equity is required.
16 The bank restructuring plans of the major state-owned banks approved by the European Commission determined
a minimum return on equity of 7-10 percent.
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 61
Box 1. Financial Development Index
The financial development index (FD index) is constructed to capture indicators of financial institutions
(FI) and financial markets (FM) across three dimensions: access, depth, and efficiency (see table). The FI
and FM are based on six sub-indices: financial institutions access (FIA), financial institutions depth (FID),
financial institutions efficiency (FIE); the financial markets access (FMA), financial markets depth (FMD),
and financial markets efficiency (FME). These sub-indices in turn are based on the indicators listed in
the table.
The dataset comprises annual data for the period 1980-2013 for 176 countries (25 advanced, 85
emerging, and 66 low-income developing countries).1/
The data for the indicators are winsorized at the 5th and 95th percentiles to avoid extreme values driving
the results. Each indicator is normalized between zero and one, using a global mini-max procedure that
relates country performance to global minima and maxima across all countries and years. For all
indicators higher values mean greater financial development.
Sub-indices are constructed as weighted averages of the underlying indicators, where the weights are
obtained from principal component analysis, reflecting the contribution of each underlying indicator to
the variation in the specific sub-index. Sub-indices are combined into higher indices also using
principal component analysis.
The result is a relative ranking of countries on depth, access, and efficiency of financial institutions and
financial markets, on the development of financial institutions and markets, and on the overall level of
financial development. The minimum and maximum values of all indices are zero and one, respectively.
More details can be found in “Rethinking Financial Deepening: Stability and Growth in Emerging
Markets”.
1/ A large portion of the empirical work is based on the World Bank’s Global Financial Development database,
which requires a long lag to update with end 2013 figures released in late September 2015.
FINANCIAL INSTITUTIONS FINANCIAL MARKETS
1. Private-sector credit (% of GDP) 1. Stock market capitalization to GDP
2. Pension fund assets (% of GDP) 2. Stocks traded to GDP
3. Mutual fund assets (% of GDP) 3. International debt securities of governments (% of GDP)
4. Insurance premiums, life and non-life (% of GDP) 4. Total debt securities of nonfinancial corporations (% of GDP)
5. Total debt securities of financial corporations (% of GDP)
1. Branches (commercial banks) per 100,000 1. Percent of market capitalization outside of top 10 largest
adults companies
2. ATMs per 100,000 adults 2. Total number of issuers of debt (domestic and external,
nonfinancial corporations, and financial corporations)
1. Net interest margin 1. Stock market turnover ratio (stocks traded/capitalization)
2. Lending-deposits spread
3. Non-interest income to total income
4. Overhead costs to total assets
5. Return on assets
6. Return on equity
Source: Rethinking Financial Deepening: Stability and Growth in Emerging Markets
Construction of the Financial Development Index
DEP
TH
EFFIC
IEN
CY
AC
CESS
REPUBLIC OF SLOVENIA
62 INTERNATIONAL MONETARY FUND
Box 2. Financial Development, Growth and Stability
The analysis in the IMF paper suggests that financial development increases growth and stability, but
the effects weaken at higher levels of financial development, and eventually become negative. And that
financial deepening drives the weakening effect. Fast deepening of financial institutions can lead to
economic and financial instability, as it encourages greater risk-taking and high leverage (including
through reduced capital buffers in banks), if not countered by adequate regulation and supervision.
The relation between finance and economic growth (as well as economic volatility and financial
stability) is estimated using the form
�̇�𝑖𝑡 = 𝛼 + 𝛽0𝐹𝐷𝑖𝑡 + 𝛽1𝐹𝐷𝑖𝑡2 + 𝛽2(𝐹𝐷𝑖𝑡 ∙ 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖) + 𝛽3𝑋𝑖𝑡 ,
where y is the per capita real GDP growth, FD is the financial development index (or sub-component),
and its square, Interact accounts for additional interactions, and X for a set of controls variables (initial
income per capita, education (secondary school enrollment), trade-to-GDP, consumer price index
inflation, government consumption-to-GDP ratio (as proxy for macroeconomic stance), and foreign
direct investment-to-GDP ratio). Using a dynamic system generalized method of moments (GMM)
estimator the equation is estimated over non-overlapping five-year periods in the 1980-2010 range,
based on a 128-country sample.
The same method is applied for economic volatility and financial stability as dependent variables,
rather than economic growth. In that case, economic volatility was measured by the rolling five-year
standard deviation of growth, and financial stability was approximated by distance to distress, defined
as [capital to assets + return on assets / standard deviation of return on assets].
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 63
Figure 1. Slovenia: Private Sector Credit
(In percent)
0
50
100
150
200
250
300R
OM
HU
N
LV
A
PO
L
BEL
ZA
F
BR
A
CH
L
IRL
ITA
FIN
MLT
NLD
PR
T
2010 2013 2014 SVN 2015
Sources: IFS and WEO
Private Sector Credit to GDP
(In percent)
ROM
HUN
CZE POL
SVN
BEL
BGR
ZAF
EST
BRA
HVR
CHL
DEU
IRL
AUT
ITA LUX
FIN
FRA
GRC
NLD
ESP
PRT
-40
-20
0
20
40
60
80
100
120
140
160
0 20 40 60 80 100 120 140
2004-2
010 c
um
ula
tive
g
rro
wth
rate
2014
Sources: IFS and WEO
Private Credit to GDP
(In percent)
SVN 2015
REPUBLIC OF SLOVENIA
64 INTERNATIONAL MONETARY FUND
Figure 2. Slovenia: Financial Development Index, Sub-Indices
Figure 2. Slovenia: Financial Development Index, Sub-Indices
Source: IMF staff
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Development Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Institutions Development Index
0
0.1
0.2
0.3
0.4
0.5
0.6
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Markets Development Index
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 65
Figure 3. Slovenia: Financial Development Sub-Indices Components
Figure 3. Slovenia: Financial Development Sub-Indices Components
Source: IMF staff
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Institutions Depth Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Markets Depth Index
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Institutions Access Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Markets Access Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Institutions Efficiency Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
EA SVN CEE EME SVN 2014 est SVN 2015 est
Financial Markets Efficiency Index
REPUBLIC OF SLOVENIA
66 INTERNATIONAL MONETARY FUND
Figure 4. Slovenia: Components of Financial Development Index, 2013
Normalized Variables
Source: IMF staff
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Private sector
credit to GDP
Pension Fund
Assets to GDP
Mutual Fund
Assets to GDP
Insurance
premiums (life +
non-life) to GDP
EA SVN CEE EME
Financial Institutions - Depth
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Stock market
capitalization
to GDP
Stocks
traded to
GDP
International
Debt
Securities of
Government
as % of GDP
Debt
Securities of
financial
corporation
as % of GDP
Total Debt
Securities of
non-fin.
corps as % of
GDP
EA SVN CEE EME
Financial Markets - Depth
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Branches per 100,000 adults
(commercial banks)
ATMs per 100,000 adults
EA SVN CEE EME
Financial Institutions - Access
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Perc
ent o
f mark
et
cap
italiz
ation
outs
ide o
f to
p 1
0
larg
est
co
mps
To
tal n
um
ber
of
issu
ers
of d
eb
t
(do
mest
ic a
nd
ext
ern
al,
NFC
s and
financi
al c
om
ps)
EA SVN CEE EME
Financial Markets - Access
0.0
0.2
0.4
0.6
0.8
1.0
Net In
tere
st
Marg
in
Lend
ing
-Depo
sits
Sp
read
No
n-I
nte
rest
Inco
me to
To
tal
inco
me
Ove
rhead
Co
sts to
To
tal A
ssets R
OA
RO
E
EA SVN CEE EME
Financial Institutions - Efficiency
0.0
0.1
0.2
0.3
0.4
0.5
Turnover ratio (turnover/capitalization) for stock market
EA SVN CEE EME
Financial Markets - Efficiency
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 67
Figure 5. Slovenia: Comparative Evolution of Institutions and Markets
Figure 5. Slovenia: Comparative Evolution of Institutions and Markets
Source: IMF staff
1995
1996
1997
19981999
2000
20012002
2003
2004
2005
2006
2007
2008
2009
2010
2011
20122013
2014 est
0.2
0.3
0.4
0.5
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Financi
al M
ark
ets
Financial Institutions
Slovenia - Institutions and Markets Indices(1995-2015)
2015 est
1995
1996
1997
19981999
2000
2001
2002
20032004
2005
2006
2007
2008 20092010
20112012
2013
0.2
0.3
0.4
0.5
0.6
0.4 0.5 0.6 0.7 0.8
Financi
al M
ark
ets
Financial Institutions
EA - Institutions and Markets Indices(1995-2013)
1995
1996
1997
1998
1999
2000
2001 20022003
2004
20052006
2007
2008200920102011
2012
2013
0.0
0.1
0.2
0.3
0.4
0.2 0.3 0.4 0.5 0.6 0.7
Financi
al M
ark
ets
Financial Institutions
CEE - Institutions and Markets Indices(1995-2013)
1995
1996
1997
1998199920002001
2002
20032004
2005
2006
2007
20082009
201020112012
2013
0.0
0.1
0.2
0.3
0.4
0.2 0.3 0.4 0.5 0.6
Financi
al M
ark
ets
Financial Institutions
EME - Institutions and Markets Indices(1995-2013)
REPUBLIC OF SLOVENIA
68 INTERNATIONAL MONETARY FUND
Figure 6. Slovenia: Financial Development Impact
Figure 6. Slovenia: Financial Development Impact
Source: IMF staff
ROMHVR SVK BGRCZE
SVN 2014
SVN
POLHUN
IRL
USA
JPN
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Effect
on G
row
th R
ate
(%)
Financial Development Index
Financial Development and Growth
(2013)
SVN 2015
ROMHVR
SVK BGRCZE
SVN 2014
SVN
POLHUN
IRL
USA
JPN
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Gro
wth
Vo
lati
lity
Financial Development IndexSources: Segoe UI - Size 18
Financial Development and Growth Volatility
(2013)
SVN 2015
ROMHVR SVK
BGR
CZE
SVN 2014SVN
POL HUN IRL
USA
JPN
-30
-25
-20
-15
-10
-5
0
5
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Effect
on D
ista
nce
to
Dis
tress
Financial Institutions Depth
Financial Development Financial Stability
(2013)
SVN 2015
REPUBLIC OF SLOVENIA
INTERNATIONAL MONETARY FUND 69
References
Bank of Slovenia, 2015, “Report on the Causes of the Capital Shortfalls of Banks”, March.
Cecchetti, Stephen G and Enisse Kharroubi, 2015, “Why does financial sector growth crowd out real
economic growth?”, Bank for International Settlements, Working Paper No. 490.
Čihák, Martin, Aslı Demirgüç-Kunt, Erik Feyen, Ross Levine, 2012, “Benchmarking Financial Systems
around the World”, World Bank, Policy Research Working Paper No. 6175.
Cournède, Boris, Oliver Denk, Peter Hoeller, 2015, “Finance and Inclusive Growth”, OECD, Economic
Policy Paper No. 14.
Impavido, Gregorio, Heinz Rudolph, and Luigi Ruggerone, 2013, “Bank Funding in Central, Eastern
and South Eastern Europe Post Lehman: a “New Normal”?” IMF, Working Paper No. 13/148.
International Monetary Fund, 2015, “Advancing Financial Development in Latin America and the
Caribbean”, Regional Economic Outlook: Western Hemisphere.
International Monetary Fund, 2015, “Vulnerabilities, Legacies, and Policy Challenges”, Global
Financial Stability Report.
International Monetary Fund, 2013, “Financing Future Growth: the Evolving Role of
Banking Systems in CESEE”, Central, Eastern and Southeastern Europe, Regional Economic Issues.
International Monetary Fund, 2012, “Republic of Slovenia: Financial System Stability Assessment, IMF
Country Report No. 12/325.
Luigi Zingales, “Does Finance Benefit Society?”, 2015, Harvard University.
Sahay, Ratna, Martin Čihák, Papa N’Diaye, Adolfo Barajas, Ran Bi, Diana Ayala, Yuan Gao, Annette
Kyobe, Lam Nguyen, Christian Saborowski, Katsiaryna Svirydzenka, and Seyed Reza Yousefi, 2015,
“Rethinking Financial Deepening: Stability and Growth in Emerging Markets,” IMF, Staff Discussion
Note No. 15/8.
The Journal for Money and Banking, 2015, “Banking Sector at the Crossroads: Challenges for the
Future”, The Bank Association of Slovenia, No. 11.
The Journal for Money and Banking, 2010, “Corporate Governance of Banks”, The Bank Association
of Slovenia, No. 11.
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